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About 35% of current jobs in the UK are at high risk of computerisation over the following 20 years, according to a study by researchers at Oxford University and Deloitte. Go to http://www.bbc.co.uk/news/technology-34066941 and type your job title into the search box below to find out the likelihood that it could be automated within the …
I learned to perform under pressure at thirteen, sleeping rough on the street, where reading a room wrong could cost you far more than a meal—sometimes it was the difference between making it through the night and not. So when I tell you that most of …
“Why isn’t common sense more common at work?”
I’ve heard some version of this question more times than I can count, and it’s only increasing.
I’ve asked it myself. I’ve been at nearly every rung of the corporate ladder, from working the cash register to sitting in board meetings. And it’s often people in the latter who could use a reminder to take a break from slide decks and spreadsheets and make decisions using their head—and yes, even their heart.
To get there, it helps to have real examples. I get plenty of them whenever I follow up: “What happened most recently to make you think common sense is missing?” Because there’s usually something very specific underneath that generalization.
Where common sense goes missing
Sometimes it’s small. An employee is expected to be in the office by 8:30, but schedules a doctor’s appointment that means they won’t be in until 9. There’s nothing urgent or pressing that requires them to be there at 8:30. If you were their manager, what would you say? Maybe: “It’s 30 minutes, just let it go.” Or “Just record it as sick time.” Or “Can you stay 30 extra minutes later to make it up?” All fairly reasonable. But there are managers who’d roll their eyes and write up the team member instead.
Sometimes it’s bigger. A manager is furious that a direct report didn’t send an important client email out. They fire off an all-caps “CALL ME ASAP” message and launch into “How could you forget that?! That’s unacceptable!” Their report pauses and says, “But I did send it—I copied you on it.” What should the manager do? It seems obvious: check their email and, if they missed it, apologize and ask next time instead of assuming. But there are managers who wouldn’t ever do that, and instead sputter, “Well, you should have messaged me to make sure I saw it.”
And sometimes it goes all the way up the ladder. A CEO sees a viral video of a creator posing as a remote employee—shopping, hair appointment, napping during the workday—and treats it as proof of how their own workforce operates. The CEO could ask their own remote team members what their days are like, or look at the research showing what suddenly requiring everyone to be back in an office can do to performance, engagement, and retention. In other words, verifying what’s actually happening instead of rewriting a policy based on a TikTok. But plenty of CEOs skip that step.
These are three scenarios out of the dozens that I’ve heard—and others likely come to mind for you, too. Which brings me back to the original question: Why isn’t common sense more common?
Three reasons common sense gets overridden
Common sense isn’t gone. But it gets overridden, especially by three common culprits.
· Ego. In the email example, the manager could’ve said, “You’re right, my mistake.” Instead, they made up a new rule: From now on, every email concerning them also requires a message confirming it went out. As absurd as it sounds, it feels safer than apologizing. Other times, ego means chasing credit—so a manager requires sign-off on every decision, or their name on a deck instead of the person who actually did the work. Or it’s the reverse: Decide something alone and get it wrong, and it’s your job on the line; make three people agree to it, and now it’s everyone’s. Either way, the decision stops being about what makes sense and starts being about who gets noticed.
· Mistrust. This is the example of the CEO and the viral video. One person, whose goal is only views and likes, becomes the reason for a policy that impacts everyone else who showed up and did the job. The same instinct plays out smaller every day: One person pads an expense report, and instead of dealing with that person, there’s a new rule for everyone, including the 99.9% who were never the problem. You’ve just managed to the lowest common denominator, and people who don’t feel trusted rarely act like they are.
· Fear. This is the 8:30 story. The manager isn’t actually worried about 30 minutes. They’re worried that if their own boss notices their team member walk in at 9, they’ll look bad for allowing it. And in their head, it snowballs from there. One late morning becomes a pattern, that pattern becomes the new standard for everyone, and then they become the manager who can’t get anyone to show up on time. So instead of treating it like the routine, human situation it is, they treat it like a precedent-setting crisis.
Most examples I hear include all three at once, and often stem from the top. Managers who are told “double-check with me before you send that” and “you’ll set a precedent” by senior leadership start to believe their judgment is a liability, not an asset. So they stop using it. And then everyone wonders why a 30-minute request takes a week, and why employees feel like they’re dealing with a rule book instead of an actual human being.
How to bring common sense back
Once you’ve climbed multiple rungs of the corporate ladder, it’s easy to forget what things look like to the ones below you. So try this instead. Before you escalate a situation, institute a new policy, or loop in four people to gut-check a decision, ask yourself this: If I were in their shoes, what would I want to see happen? This is the Pause-Consider-Act framework in action.
Go back to the three scenes above. Consider what those team members would say: “Let me go to the doctor, and I’ll show up healthier.” “Check the sent folder and apologize, and I’ll respect you more.” “Ask what my day is like working remotely, and I’ll give you my breakdown.” Usually, they’d say the obviously reasonable thing, because most of the time, the reasonable thing actually is obvious. We just stopped trusting ourselves to do it.
This test doesn’t pretend nothing will ever go wrong. People will take advantage of flexibility sometimes, or forget to send emails. But you’re not managing for the exception first. You’re building trust, so that when you do have to address the exception, it feels fair instead of arbitrary. Having standards and approving a late start aren’t opposites; they’re both just part of managing the real people you need to get the work done.
Common sense is a muscle, and it atrophies when you stop using it and start outsourcing it, whether to policy, to committees, or “let me get back to you.” The cost of not using it is decisions that ignore the real people they’ll affect—and, as I can attest, those people tell everyone else they know.
And here’s my answer back: “Common sense isn’t rare, but managers willing to trust it are.”
At the end of July, a debut crime novel became the most expensive casualty so far in the publishing industry’s war over AI. Fourteen publishing houses had bid for the manuscript, with the winning offer delivering a $2 million contract for the author. But when rumors began to circulate that the book had been written with the help of artificial intelligence, his own agents withdrew the book.
The author denies using AI and has pointed out that the manuscript had been read by editors and acquisitions teams across the industry. What they saw on the page excited them enough to open their wallets. But while the words and sentences remained the same, the emergence of concerns about their source was enough to kill the sale, and possibly the author’s future career.
It was at least the third such major scandal this year. The ferocity of the public response to these stories is understandable. Part of what a reader buys when they purchase a novel is the human being behind it. The experience of engaging with a work of art is often anchored in a meeting of minds, so the idea that AI contributed to the output can feel like a betrayal.
But these legitimate concerns risk fueling a more general, and far less well-grounded, presumption that any AI involvement in any output that humans once produced is a form of cheating. The result is a strange cultural moment: a tool we are constantly told we must learn to use is becoming something people fear being caught using.
When Anthropic announced last month that Claude will weave an invisible watermark into the text it processes, the public response reflected deep divisions. Some people celebrated: “The only reason you wouldn’t want this is to lie to people,” as one commenter put it. Others canceled their subscriptions, fearing that anyone who lets AI touch their writing can now be branded with a modern scarlet letter.
Employees are left in a double bind. They can avoid using valuable productivity tools and risk criticism from pro-AI bosses and colleagues. Or they can use them and worry that they will draw the ire of the crowd. Business leaders must steer a careful path through this difficult landscape. The first step is to set aside the question “Did you use AI?” Instead, we need to focus on a subtly but importantly different criterion: “What did you use AI for?”
Why care who produced the work?
There are three distinct things you might want to know about a given piece of work in a business context:
- Is the output good?
- What does the output tell us about the capability of the person who produced it?
- What does the process of producing it do for that person’s capabilities?
Historically, we often answered all three by looking at the same piece of work. If a junior analyst produced a brilliant report, we saw that the report was good, we could infer from its quality that the analyst was good, and it was reasonable to think that doing the work was helping the analyst develop their core skills.
Generative AI shows us that the connection between those three questions can break. A report can be excellent without the analyst having done anything apart from prompting a large language model (LLM). And in such a case, producing an excellent report does not help the analyst become a better analyst; at most, it helps them develop their ability to prompt LLMs.
Given this broken connection, it is a mistake to use provenance as a proxy for all three questions. Instead, leaders should follow a more nuanced and fine-grained approach, in which the purpose of the work plays a crucial role in deciding how AI should be involved in doing it.
Production: Judge the work
When the purpose is purely production, the question is simple: Is the work good? So, whatever the context, business leaders should define the criteria for what counts as good work and then judge the output against those standards.
A corollary of this is that the method of production is irrelevant. The employee could have used Claude or the ancient art of divination to produce the output—it doesn’t matter. The only thing that matters is whether the work meets the required standard.
But indifference to method cuts both ways: If using the tool earns no penalty, it also offers no shelter from responsibility. Once an employee has checked and submitted a piece of work, it is theirs. “The AI did it” does not excuse an error, and “I did it by hand” does not excuse mediocre work.
So the rule follows directly: For production work, judge the output and enforce ownership.
Assessment: Test the person
Sometimes, however, it is not the quality of the work that is ultimately important; rather, what matters is what the output says about the capabilities of the person producing it. Obvious examples of such moments are the job interview and the promotion discussion.
Here, provenance begins to become more relevant because for the output to serve as evidence of capability, there must be some meaningful connection between the person and the output. But instead of simply asking whether AI did it, it is much more informative to ask the person to talk you through the output. Why did they choose this structure? Why was this evidence important? What are the weak points of the argument?
This is particularly important given the current limitations of AI detection. Anthropic explicitly says that its watermark “can only determine that Claude was likely involved with the content at some point.” The extent and nature of the involvement is open—but it is precisely this that needs to be understood if we are using output to assess the capabilities of the person who produced it.
Of course, sometimes it may be important to know whether someone can perform a task without assistance. But then that should just be tested directly, instead of turning every ordinary deliverable into a purity test.
Development: Protect the learning
Learning by doing has always been a very important part of development, and efficiency is often legitimately sacrificed to the goal of skill development. Even if it costs the firm more, junior employees will be given some work precisely because doing it develops the expertise needed for more senior work later.
But if the junior employee is simply palming off the tasks to AI, the work loses its development function. For example, in a randomized study of developers learning an unfamiliar Python library, participants given AI assistance scored 17% lower on a subsequent test of conceptual understanding, code reading, and debugging. Companies may get better output today, but they will be weakening the supply of people capable of using that output tomorrow.
Further, the study found that participants who remained cognitively engaged—asking conceptual questions or requesting explanations of generated code—preserved much more of the learning. This means that it is not as simple as saying that using AI to produce output is bad for development—much depends on how the AI was used.
Therefore, leaders need to be intentional about how they develop people through work and with the use of AI. The only constant here is that development work should contain deliberate friction, because that is required for learning. Beyond that, there is no single right answer: Sometimes the employee should use AI freely. Sometimes AI should explain but not solve. Sometimes a first attempt should be unaided. The right constraint depends on the capability you are trying to build.
4 things to do instead of making AI use a purity test
Here are four things leaders can do right now to develop an AI policy that helps both the organization and its employees:
- Classify the work. Before setting an AI rule, ask whether the primary purpose is production, assessment, or development. Many jobs contain all three, but individual tasks usually lean toward one.
- Match the rule to the purpose. Production work should normally optimize for quality and accountability. Assessment should expose the capability being assessed. Development should preserve enough human practice to build the capability you will need later.
- Make people defend important work. Do this routinely, not as an AI interrogation. If employees regularly explain their reasoning, managers can observe judgment without turning provenance into a verdict.
- Treat detection as evidence, not judgment. A watermark may tell you that an AI system touched the work. It cannot tell you whether the work is good, whether the employee understands it, or whether using AI helped or harmed their development.
Focus on the things that matter
A business runs on quality, capability, and learning—and provenance is a weak proxy for all three. Ask what the AI was used for, and judge accordingly. Leave questions of authenticity to artists and their patrons.
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