Most AI at work is bullshit

Most AI work inside companies is bullshit.

It's not because AI is bad, or that people are lazy. It's not even really that the tools are overhyped, though, okay, a lot of them are. Most AI at work is bullshit because companies are trying to adopt AI without changing how work is managed.

That distinction matters. A lot of companies think they are becoming AI-first because more employees are using AI tools. But AI usage is not transformation.

People inside your company are rewriting emails faster. People are summarizing meetings. Someone builds a little demo. Someone creates a prompt library. Someone posts in Slack that they used Claude to think through a problem. For a few weeks, the company really feels like it is moving.

But the work itself often stays the same.

This is the strange part about AI adoption. AI is supposedly the thing that will change everything, yet most companies use it to preserve the existing system. The same workflows, meetings, dashboards, job descriptions; the same incentives and very similar results. The only difference is that now some of the work is slightly faster, slightly more automated, and slightly harder to evaluate.

That’s how you get bullshit AI work: work that creates the feeling of progress without creating much actual value.

The way I look at it, there are a few levels of bullshit AI at work, and they largely go along with whatever the level of AI adoption is at your company.

AI adoption stages

A company with no real AI initiative has one kind of bullshit. A company where leadership tells everyone to “use AI” has another. A company where everyone is vibe coding has another. And the most advanced companies eventually run into a harder truth: this was never mainly about tools. It was about change management.

BS 1: The vague AI push

The first kind of bullshit is the vague AI push.

This is when leadership announces that everyone should use AI more. The message usually sounds right: "We need to become AI-first. We need to automate repetitive work. We need to free people up for more strategic work. We need everyone to start experimenting with AI."

None of this is wrong. The problem is that it's incomplete.

What happens after the announcement? Usually, not much. No dedicated time. No clear goals, or budget. No operating rhythm. No explanation of what good looks like. No change to team priorities. Just a message, some emojis, maybe a new Slack channel, and a general feeling that everyone is now expected to figure it out by themselves.

The company thinks it has started becoming AI-first. In reality, it sent a Slack message. The expectations and pressure are high, the directions are low.

I don't actually know where I took this from. Sorry.

This is not an AI strategy. It's a wish.

If leadership wants AI to become real, they have to make it real in the calendar, the budget, and the operating system of the company. At Omnisend, we eventually created an initiative called AI as a Habit. The idea was simple: AI was not something people were supposed to squeeze into the gaps between meetings. It was part of the job.

We allocated a percentage of people’s time to working with AI. We carved it into their planning; created actual space for them to explore, learn, and apply AI. This matters because time is the most honest signal a company can send. If something is important, it gets time. If it gets no dedicated time, it is not important. It's just decoration.

Omnisend's "AI as a Habit" initiative

We also created monthly AI Days.

On the first Friday of every month, people cleared their calendars and focused on AI-related improvements. Most meetings were cancelled. Some of the day was education, some of it was experimentation, and some of it was teams sitting together and asking what painful part of their work could be improved.

The exact format dependds on what your company or your team can allow, and honestly it matters less than the signal. AI became real work.

And this is the thing leadership often misses: people do not change how they work because leadership says “please innovate.” They change when the system around them changes. If you tell people AI is important but give them no time to work on it, they will understand the real message: AI is important, but not as important as everything already on your calendar.

So yes, at the beginning, you probably have to force the time a little bit. Not in a creepy way. But in the sense that you put it on the calendar and say: this is the time. Clear your schedule. Work on this. No opt-outs.

Otherwise, you're asking for it to happen organically, which means it will happen very slowly, which means it probably will not happen at all.

BS 2: Productivity theater

The second kind of bullshit is productivity theater.

This usually happens after people have started using AI for small individual tasks. They polish emails, summarize documents, rewrite Slack messages, use meeting note tools. Draft posts. Turn messy notes into something presentable.

Some of this is pretty useful. I use AI for a lot of these things myself. But the thing is that individual productivity is a good starting point, but really a bad destination.

The problem is that companies often mistake individual activity for organizational improvement. Someone says they saved 30 minutes writing an email. Great. But did the team get better? Did the customer experience improve? Did the reply rate go up? Did the sales cycle shorten? Did the campaign perform better? Did the decision improve?

Often, no one knows. In fact, an MIT study [pdf] showed that 95% of enterprise organizations, that had invested $30-$40 billion in AI saw zero return.

The true state of AI?

This is where self-reported productivity becomes dangerous. People are genuinely bad at estimating time saved. They are even worse at estimating impact. And when leadership asks for AI success stories, people learn to tell stories that sound like success.

“I used AI to do this faster” sounds good. But faster is not always better. Sometimes it's just faster.

A person can write emails faster while the team’s positive reply rate stays exactly the same. A marketer can produce more variations while conversion does not move. A manager can summarize meetings faster while the same decisions still get delayed.

The outputs improved, but the outcomes did not. That is productivity theater.

The fix is to reward impact, not AI usage. At Omnisend, we introduced an AI Salary Bump. The idea was not to reward people for using AI (creating the wrong incentive). The idea was to reward people who used AI to make their work, or their team’s work, more efficient, more impactful, and adopted by others.

The Omnisend AI Salary Bump

Efficient means it saves time or money. Impactful means the work actually gets better. Adopted means it survives outside your own personal workflow.

If you use AI to save yourself 20 minutes once, good for you. If you use AI to change how your team works every week, that's different. The question should not be: did you use AI? The question should be: how did you use AI to actually improve your work?

There is also a cultural point here. This was not framed as a punishment for people who do not use AI. That distinction matters. Because the moment AI adoption becomes a compliance exercise, people will perform compliance. They will use AI because someone asked them to use AI. They will produce AI activity because activity is what gets noticed.

But if AI becomes a way to create and reward actual impact, serious people will treat it seriously.

BS 3: Shiny project mode

The third kind of bullshit is shiny project mode.

This is what happens when companies move from individual productivity to building things. Teams try twenty tools. Someone makes a demo. Someone vibe codes a small app. Someone else builds a workflow. By Friday afternoon there is something that kind of works, and everyone feels the future has arrived.

Then Monday comes, and everyone goes back to their real work.

The demo sits there. No one owns it. No one maintains it. No one measures whether it works. No one adopts it. The next AI Day comes around and the cycle repeats.

Build. Abandon. Repeat.

This stage is common because building with AI feels like progress. It gives people something to show. It creates the feeling of momentum. And to be fair, experimentation is necessary. People need to play with tools before they can understand them. They need to try things that don’t work; they need that messy phase.

But experimentation only matters if it compounds. Otherwise it is just expensive curiosity.

The fix is to move from projects to products.

The project vs product mindset

A project has a demo. A product has users. A project has an output. A product has an outcome. A project can be abandoned after the presentation. A product needs ownership, maintenance, adoption, feedback, and some way of knowing whether it's working.

This was one of the biggest shifts for us. We moved away from “what can we build during AI Day?” toward “which workflows should we improve this quarter?”

That sounds less exciting, because it is. Real AI work becomes boring very quickly. But boring is usually where the value is.

A prompt that helps someone write an email faster is a project. A system that increases positive response rates from 2% to 4% is closer to a product. The difference is not in the technology, but rather in the expectation.

One -- the project -- is about creating something. The other -- the product -- is about changing something.

That’s why we eventually moved toward decentralized AI time. No more one-size-fits-all company-wide AI Days on the first Friday of each month. Each department could decide when its AI Days, half-days, or working sessions made sense. More importantly, departments started focusing on core workflows on a quarterly basis, with KPIs tied to outcomes.

This is where it becomes less “AI event” and more operating model. It’s not about the cool thing you built on Friday. It’s about whether anyone is still using it three weeks later.

BS 4: Everyone should be vibe coding

The fourth kind of bullshit is the company science fair era.

This is where everyone is encouraged to build. Everyone gets introduced to tools like Lovable, v0, Cursor, Replit, Claude Code, Hostinger Horizons, or whatever the current favorite is. Non-technical teams start vibe coding apps. People build little dashboards, internal tools, landing pages, Chrome extensions, automations, forms, workflow helpers.

Again, some of this is good. Vibe coding can be genuinely powerful. I am not anti-vibe coding. I'm a vibe coder myself; I use these tools regularly, and they have changed how I work.

But the trap is assuming that because everyone can build, everyone should build.

The reality is that AI democratized access. It didn't democratize outcomes.

That distinction matters. More people can now create software-like things. But that does not mean everyone has the patience, judgment, taste, technical intuition, or interest to turn those things into something useful.

A non-technical person can spend three days debugging a login issue in a vibe-coded app. Is that empowering? Maybe. Is it the best use of their time? Maybe not. This is the question companies need to ask much more often:

"What is the best use of this person’s time?"

Not “can they build this with AI?” Increasingly, the answer will be yes. The better question is: should they?

Sometimes the right model is not “everyone becomes a builder.” Sometimes the right model is that domain expert becomes the product thinker, a skilled builder is the execution, and AI is the acceleration.

The domain expert understands the problem. They know the workflow. They know what good looks like. They know what would actually save time or improve quality. But that does not mean they should be the person fighting with deployment, databases, permissions, authe

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