Remember when you first heard that your company was going AI-First,
Perhaps it came from all those hands feeling different from others. “By Q3, every team should have integrated AI into their core workflows,” the CEO said, and the energy in the room (or on Zoom) shifted. You saw a mixed wave of excitement and concern in the crowd.
Perhaps you were one of the curious ones. Maybe you’ve already created a Python script that summarizes customer feedback, saving your team three hours every week. Or maybe you stayed up late one night to see what would happen if you combined a dataset with a large language model (LLM) prompt. Maybe you’re one of those people who has already let curiosity take you to unexpected places.
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 know it yet, but something fundamental has changed in how innovation happens inside your company.
How does innovation happen?
The actual change rarely looks like the PowerPoint version, and almost never follows the org chart.
Think about the last time something really useful was spread in the workplace. It wasn’t because of the salesperson’s pitch or strategic initiative, was it? More likely, someone stayed up late one night when no one was looking, found something that beat rush hour, and mentioned 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 was not trying to exert strategic influence. He just wanted to get home quickly to his children. The ops 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 watch what happens when leadership notices. What used to be natural and organic has become mandatory. And something that once worked because it was free suddenly isn’t as effective the moment it’s measured.
great reversal
It usually starts quietly. Often when a competitor announces new AI features, – like AI-powered onboarding or end-to-end support automation – it claims 40% efficiency gains.
The next morning, your CEO calls an emergency meeting. The room becomes quiet. Someone clears his throat. And you can feel that everyone is doing mental math about their job security. “If they’re 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. Tomorrow.”
Here’s how that message typically ripples out on an org chart:
- In the C-Suite: “We need an AI strategy to stay competitive.”
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At the VP level: “Every team needs an AI initiative.”
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At the manager level: “We need a plan by Friday.”
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At your level: “I just want to find something that looks like AI.”
Each translation adds pressure while decreasing understanding. Everyone still cares, but that translation changes the intentions. What starts out as a question worth asking becomes a script that everyone blindly follows.
Ultimately, it is the demonstration of innovation that replaces the thing. there is a strange pressure Look Like you’re moving fast, even when you’re not sure where you’re actually going.
This is repeated across industries
One competitor announced they were going AI-first. Another has published a case study about replacing support with an LLM. And a third shares a graph showing productivity gains. Within days, the same message is echoing in boardrooms everywhere: “We must do this. Everyone else is already doing it, and we can’t afford to be left behind.”
So the work begins. Then come task forces, town halls, strategy documents and goals. Teams are asked to contribute to the initiative.
But if you’ve been through this before, you know there’s often a difference between which companies announcement of and what they really are to doBecause the press releases don’t mention the pilots who stop, or the teams who quietly return to the old path, or even the equipment that is used once and discarded, Maybe you know someone who was on one of those teams, or maybe you’ve been on one yourself,
These are not failures of technology or intent. ChatGPT works fine. And teams want to automate their tasks. These failures are organizational, and they happen when we try to mimic results without understanding what created them in the first place.
And so while everyone is innovating, it becomes almost impossible to tell who is really doing it.
two types of leaders
You’ve probably seen both, and it’s pretty easy to tell which type you’re dealing with.
Someone spends an entire weekend building a prototype. They try something new, fail half the time, and still show up on Monday saying, “I built this thing with the cloud. It crashed after two hours, but I learned a lot. Want to see? It’s pretty basic, but it might solve the thing we talked about.”
They try to make sense. You can tell they’ve really spent time with the AI, and struggled with the prompts and hallucinations. Instead of trying to speak definitively, they talk about what broke, what almost worked and what they’re still figuring out. They invite You should try something new because it feels like there is room for learning. This is what participatory leadership looks like.
Another sends you a directive in Slack: “Leadership wants every team to be using AI by the end of the quarter. Plans are due by Friday.” They enforce compliance with the decision that has already been taken. You can even hear it in their language, and how sure they sound.
The curious leader creates momentum. The protester creates resentment.
what really works
You probably don’t need anyone to tell you where AI works. You already know because you’ve seen it.
- Customer Support: LLMs really help in terms of tier 1 tickets. They understand intent, draft simple responses, and understand the complexity of the passage. Not perfectly, of course – I’m sure you’ve seen failures – but well enough that it counts.
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Code Help: At 2 a.m., when you’re half-witted and your AI assistant suggests exactly what you need, it feels like you have an over-caffeinated junior programmer who never evaluates your forgotten semicolons. You save minutes, then hours, then days.
These small, cumulative wins add up over time. These aren’t the impressive changes promised to the deck, but they are improvements you can count on.
But outside these areas things get murky. AI-powered repops? Fully automated forecasting? You’ve seen those demos, and you’ve also seen the excitement fade once the pilot actually launches.
Have the creators of these AI tools failed? barely. The technology is evolving, and products built on top of it are still learning to keep up.
So how can you tell if your company’s AI adoption is real? Easy. Just ask anyone involved in finance or operations. Ask what AI tools they use every day. You may get a slight pause or an apologetic smile. “Honestly? Just ChatGPT.” That’s it. Not the $50k enterprise-grade platform from last quarter’s demo or the expensive software suite in the board deck. Just a browser tab, similar to any college student writing an essay.
You can make the same confession yourself. Despite all the mandates and initiatives, your most powerful AI tool is probably the one everyone else uses. So what does this tell us about the gap between what we should be doing and what we are actually doing?
How to bring about change in your company?
You’ve probably discovered it yourself, even if no one has put it into words:
- Model what you mean: Remember that engineering director who screen-shared his chaotic, live coding session with a cursor? You learned more from watching him debug it in real time than from any sophisticated presentation, because the vulnerability extends far beyond the instructions.
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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 curious people who are quietly experimenting, figuring out through trial and error what works. And that knowledge is more valuable than any analyst report.
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Create permission (not pressure): People interested in experimenting will always find a way, and the rest will not be forced to leave. The best thing you can do is make the curious person feel safe enough to remain curious.
We live in this weird moment, torn between the AI that vendors promise and the AI that’s actually on our screens, and it’s extremely uncomfortable. The gap between product and promise is huge.
But what I’ve learned from sitting in that discomfort is that the companies that will succeed are not those that adopted AI first, but those that learned through trial and error. He lived with that discomfort for a long time, from which he got to learn something.
Where will you be six months from now?
By then, your company’s AI-first mandate will have fueled departmental initiatives, vendor contracts, and perhaps even a few new hires with “AI” in their titles. The dashboards will be green, and there will be a full slide on AI on the board deck.
But in the quiet places where your real work happens, what might have changed in a meaningful way?
Maybe you’ll be like those teams who never stopped their cool experiments. Your customer response system can catch patterns that humans miss. Your document may update itself. Chances are, if you were building before the mandate, you will still be building after it ends.
This is the invisible architecture of real progress: patient, and completely disinterested in performance. It doesn’t make for great LinkedIn posts, and it contradicts grand narratives. But it changes companies in ways that really stick.
Every organization is at the same crossroads right now: look like you’re innovating, or create a culture that fosters real innovation.
pressure to perform Innovation is real, and it’s growing. Most companies would give up and join the theatre. But some people understand that curiosity cannot be imposed and progress cannot be made. Because real change happens when no one is watching, in the hands of people who are still experimenting, still learning. This is where the future begins.
Sikki Chen is the co-founder and CEO of Runway.
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