How to avoid becoming an “AI-first” company with zero real AI usage

Think about the first time you heard that your business was going AI-first?

Maybe it was a commitment that felt different from the others. The CEO said, “By Q3, every team should have integrated AI into their core workflows,” and the energy in the room (or on the Zoom) shifted. You could see a mix of excitement and fear running through the crowd.

Maybe you were one of the curious ones. Maybe you had already built a Python script that summarized customer feedback, saving your team three hours a week. Or maybe you stayed up late one night to see what would happen if you combined a dataset with an LLM (large language model) prompt. Perhaps you are one of those who have already let curiosity lead you somewhere unexpected.

But this announcement felt different, because suddenly what had been a quiet act of curiosity was now a line in a corporate OKR. You may not have known it yet, but something fundamental had changed in the way innovation would happen within your company.

How innovation comes about

Real transformation rarely looks like the PowerPoint version and almost never follows the organizational chart.

Think about the last time something really useful was distributed at work. It wasn’t because of a sales pitch or a strategic initiative, right? More likely, someone stayed late one night when no one was looking, found something that saved hours of busy work, and talked about it at lunch the next day. “Hey, try this.” They shared it in a Slack thread and within a week half the team was using it.

The developer who used GPT to debug code wasn’t trying to make a strategic impact. She just had to go home earlier to her children. The operations manager who automated his spreadsheet didn’t need permission. He just needed more sleep.

This is the invisible architecture of progress – these informal networks where curiosity flows like water through concrete… finding every crack, every opening.

But look what happens when leadership notices. What used to be effortless and organic becomes mandatory. And what once worked because it was free suddenly isn’t as effective the moment it’s measured.

The great turnaround

It usually starts quietly. When a competitor announces new AI features, such as AI-powered onboarding or end-to-end support automation, they often claim a 40% efficiency gain.

The next morning, your CEO calls an emergency meeting. The room becomes quiet. Someone clears his throat. And you feel how everyone is doing mental math about their job security. “If they are that far ahead, what does that mean for us?”

That afternoon your company has a new priority. Your CEO says, “We need an AI strategy yesterday.”

Here’s how that message usually flows through the org chart:

• At the C-suite: “We need an AI strategy to stay competitive.”

• At the VP level: “Every team needs an AI initiative.”

• At the manager level: “We need a plan by Friday.”

• At your level: “I just need to find something that looks like AI.”

Every translation adds pressure and reduces understanding. Everyone still cares, but that translation changes the meaning. What starts as a question worth asking becomes a script that everyone follows blindly.

Ultimately, the performance of innovation replaces the thing itself. There’s a strange pressure about it Look like you’re moving quickly even when you’re not sure where you’re going.

This is repeated in all sectors

A competitor stated that they will use AI-first. Another publishes a case study on replacing support with LLMs. And a third shares a graph showing productivity gains. Within days, boardrooms everywhere will start sounding the same message: “We should be doing this. Everyone else is already doing it, and we can’t fall behind.”

So the work begins. Then come the task forces, the town halls, the strategy documents and the objectives. Teams are asked to propose initiatives.

But if you’ve experienced this before, you know that there is often a difference between which companies announce and what they actually are Doing. Because press releases don’t mention the pilots who get stuck, or the teams who quietly return to the old way, or even the tools that are used once and then abandoned. Maybe you know someone who was on one of those teams, or maybe you were on one yourself.

These are not errors in technology or intent. ChatGPT works fine. And teams want to automate their tasks. These failures are organizational in nature and arise when we try to imitate outcomes without understanding what created them in the first place.

And so if everyone is doing innovation, it becomes almost impossible to tell who is actually doing it.

Two types of leaders

You’ve probably seen them both, and it’s very easy to tell which species you’re working with.

You spend a whole weekend prototyping. They try something new, fail half way through, and still show up on Monday and say, “I built this thing with Claude. It crashed after two hours, but I learned a lot. Want to see it? It’s really simple, but it could solve that thing we were talking about.”

They try to build understanding. You can tell they actually spent time with AI and struggled with clues and hallucinations. Instead of trying to sound confident, they talk about what broke, what almost worked, and what they’re still figuring out. She invitation you to try something new because it feels like there is room to learn. This is what leadership through participation looks like.

The other sends you a directive in Slack: “Leadership wants every team to use AI by the end of the quarter. Plans are due by Friday.” They enforce compliance with a decision that has already been made. You can even hear it in their language, and how confident they sound.

The curious leader builds momentum. The performative builds resentment.

What actually works

You probably don’t need anyone to tell you where AI works. You already know it because you’ve seen it.

• Customer Support: LLMs really help with Tier 1 tickets. They understand intentions, craft simple answers, and route complexity. Not perfect, of course – I’m sure you’ve seen the failures – but good enough to matter.

• Code assistance: At 2 a.m., when you’re semi-delirious and your AI assistant suggests exactly what you need, it feels like you have an over-caffeinated junior programmer who never reviews your forgotten semicolons. You’ll save minutes, then hours, then days.

These small, cumulative gains compound over time. They’re not the impressive transformations promised in decks, but the kind of improvements you can rely on.

But outside these zones it gets dark. AI-driven revops? Fully automated forecasting? You’ve experienced those demos and you’ve also seen the enthusiasm fade away once the pilot actually starts.

Have the builders of these AI tools failed? Hardly. Technology is evolving and the products built on top of it are still learning to run.

So how can you know if your company’s AI adoption is realistic? Simple. Just ask anyone in the finance or operations department. Ask what AI tools they use every day. You may get a brief pause or an apologetic smile. “Honestly? Just ChatGPT.” That’s it. Not the $50,000 enterprise platform from last quarter’s demo or the expensive software package on the board. Just a browser tab, just like any student writing an essay.

You could make the same confession yourself. Despite all the mandates and initiatives, your most powerful AI tool is probably the same one everyone else is using. What does this tell us about the gap between what we should do and what we actually do?

How to bring about change in your business

You’ve probably discovered this yourself, even if no one has ever put it into words:

1. Model what you mean: Remember that tech director who shared her messy live coding session with Cursor on screen? You’ll learn more from watching her debug in real time than from any polished presentation, because vulnerability extends beyond guidelines.

2. Listen to the edges: You know who is actually using AI effectively in your organization, and they aren’t always the ones with “AI” in their title. They are the curious ones who have quietly experimented and discovered what works through trial and error. And that knowledge is worth more than any analyst report.

3. Creating consent (no pressure): The people who are inclined to experiment will always find a way, and the rest will not be moved by force. The best thing you can do is make the curious feel safe to stay curious.

We’re living in this strange moment, caught between the AI ​​that vendors promise and the AI ​​that’s actually on our screens, and it’s deeply uncomfortable. The gap between product and promise is large.

But what I’ve learned from sitting in that discomfort is that companies that will thrive won’t be the ones that adopted AI first, but the ones that learned through trial and error. They sat with the discomfort long enough to learn something from it.

Where will you be in six months?

By then, your company’s AI-first mandate will have set in motion department initiatives, vendor contracts, and perhaps even some new hires with “AI” in their title. The dashboards will be green and the board deck will have an entire slide on AI.

But what will have meaningfully changed in the quiet spaces where your actual work happens?

Maybe you’re like the teams that never stopped their silent experiments. Your customer feedback system can catch the patterns people miss. Your documentation may be updated automatically. Chances are if you were building before the mandate, you will also be building after the mandate fades.

That is invisible architecture of real progress: patient and completely uninterested in performance. It doesn’t make great LinkedIn posts, and it resists big stories. But it is transforming businesses in ways that are truly lasting.

Every organization is currently at the same crossroads: make sure you are innovating, or create a culture that promotes true innovation.

The pressure to to perform innovation is real and growing. Most companies will give in and join the theater. But some understand that curiosity cannot be forced and progress cannot be made. Because real transformation happens when no one is looking, in the hands of the people who are still experimenting and still learning. That’s where the future begins.

Siqi Chen is co-founder and CEO of Runway.

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