Remember the primary time you heard your organization was going AI-first?
Perhaps it got here through an all-hands that felt different from the others. The CEO said, “By Q3, every team must have integrated AI into their core workflows,” and the energy within the room (or on the Zoom) shifted. You saw a combination of pleasure and anxiety ripple through the group.
Perhaps you were considered one of the curious ones. Perhaps you’d already built a Python script that summarized customer feedback, saving your team three hours every week. Or perhaps you’d stayed late one night simply to see what would occur for those who combined a dataset with a big language model (LLM) prompt. Perhaps you’re considered one of those that’d 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 company OKR. Perhaps you didn’t realize it yet, but something fundamental had shifted in how innovation would occur inside your organization.
How innovation happens
Real transformation rarely looks just like the PowerPoint version, and almost never follows the org chart.
Think concerning the last time something genuinely useful spread at work. It wasn't due to a vendor pitch or a strategic initiative, was it? More likely, someone stayed late one night, when nobody was watching, found something that cut hours of busywork, and mentioned it at lunch the subsequent day. “Hey, do that.” They shared it in a Slack thread and, in per week, half the team was using it.
The developer who used GPT to debug code wasn’t attempting to make a strategic impact. She just needed to get home earlier to her kids. The ops manager who automated his spreadsheet didn’t need permission. He just needed more sleep.
That 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 was effortless and organic becomes mandated. And the thing that when worked since it was free suddenly stops being as effective the moment it’s measured.
The good reversal
It normally begins quietly. Often when a competitor proclaims latest AI features, — like AI-powered onboarding or end-to-end support automation — claiming 40% efficiency gains.
The following morning, your CEO calls an emergency meeting. The room gets still. Someone clears their throat. And you may feel everyone doing mental math about their job security. “In the event that they’re that far ahead, what does that mean for us?”
That afternoon, your organization has a brand new priority. Your CEO says, “We’d like an AI strategy. Yesterday.”
Here's how that message normally ripples down the org chart:
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On the C-suite: “We’d like an AI technique to stay competitive.”
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On the VP level: “Every team needs an AI initiative.”
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On the manager level: “We’d like a plan by Friday.”
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At your level: “I just need to search out something that appears like AI.”
Each translation adds pressure while subtracting understanding. Everyone still cares, but that translation changes intent. What begins as a matter price asking becomes a script everyone follows blindly.
Eventually, the performance of innovation replaces the thing itself. There’s a wierd pressure to look such as you’re moving fast, even while you’re undecided where you’re actually going.
This repeats across industries
A competitor declared they’re going AI-first. One other publishes a case study about replacing support with LLMs. And a 3rd shares a graph showing productivity gains. Inside days, boardrooms in every single place start echoing the identical message: “We must be doing this. Everyone else already is, and we will’t fall behind.”
So the work begins. Then come the duty forces, the town halls, the strategy docs and the targets. Teams are asked to contribute initiatives.
But for those who’ve been through this before, you understand there’s often a difference between what firms announce and what they really do. Because press releases don’t mention the pilots that stall, or the teams that quietly revert to the old way, and even the tools that get used once and abandoned. You may know someone who was on considered one of those teams, or you may’ve even been on one yourself.
These aren’t failures of technology or intent. ChatGPT works nice. And teams wish to automate their tasks. These failures are organizational, and so they occur once we try to mimic outcomes without understanding what created them in the primary place.
And so when everyone performs innovation, it becomes almost inconceivable to inform who’s actually doing it.
Two sorts of leaders
You’ve probably seen each, and it’s very easy to inform which type you’re working with.
One spends a whole weekend prototyping. They struggle something latest, fail at half of it, and still show up Monday saying, “I built this thing with Claude. It crashed after two hours, but I learned so much. Wanna see? It's very basic, however it might solve that thing we talked about.”
They struggle to construct understanding. You’ll be able to tell they’ve actually hung out with AI, and struggled with prompts and hallucinations. As an alternative of attempting to sound certain, they discuss what broke, what almost worked and what they’re still determining. They invite you to try something latest, since it appears like there’s room to learn. That’s what leading by participation looks like.
The opposite sends you a directive in Slack: “Leadership wants every team using AI by the tip of the quarter. Plans are due by Friday.” They implement compliance with a call that's already been made. You’ll be able to even hear it of their language, and the way certain they sound.
The curious leader builds momentum. The performative one builds resentment.
What actually works
You most likely don’t need someone to let you know where AI works. You already know since you’ve seen it.
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Customer support: LLMs genuinely help with Tier 1 tickets. They understand intent, draft easy responses and route complexity. Not perfectly, after all, — I’m sure you've seen the failures — but well enough to matter.
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Code assistance: At 2 a.m., while you’re half-delirious and your AI assistant suggests exactly what you wish, it appears like having an over-caffeinated junior programmer who never judges your forgotten semicolons. You save minutes at first, then hours, then days.
These small, cumulative wins compound over time. They aren't the impressive transformations promised in decks, however the form of improvements you may depend on.
But outside these zones, things get murky. AI-driven revops? Fully automated forecasting? You've sat through those demos, and also you’ve also seen the keenness fade once the pilot actually begins.
Have the builders of those AI tools failed? Hardly. The technology is evolving, and the products built on top of it are still learning how you can walk.
So how are you going to tell if your organization's AI adoption is real? Easy. Just ask someone in finance or ops. Ask what AI tools they use each day. You may get a slight pause or an apologetic smile. “Truthfully? Just ChatGPT.” That’s it. Not the $50k enterprise-grade platform from last quarter’s demo or the expensive software suite within the board deck. Only a browser tab, same as any college student writing an essay.
You may make this same confession yourself. Despite all of the mandates and initiatives, your strongest AI tool might be the identical one everyone else uses. So what does this tell us concerning the gap between what we're presupposed to be doing and what we're actually doing?
The right way to drive change at your organization
You've probably discovered this yourself, even when nobody's ever put it into words:
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Model what you mean: Do not forget that engineering director who screen-shared her messy, live coding session with Cursor? You learned more from watching her debug in real time than from any polished presentation, because vulnerability travels farther than directives.
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Hearken to the sides: You recognize who's actually using AI effectively in your organization, and so they're not all the time those with “AI” of their title. They're the curious ones who've been quietly experimenting, finding what works through trial and error. And that knowledge is price greater than any analyst report.
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Create permission (not pressure): The people inclined to experiment will all the time discover a way, and the remaining won’t be moved by force. One of the best thing you may do is make the curious feel secure to remain curious.
We're living on this strange moment, caught between the AI that vendors promise and the AI that really exists on our screens, and it's deeply uncomfortable. The gap between product and promise is wide.
But what I've learned from sitting in that discomfort is that firms that can thrive aren’t those that adopted AI first, however the ones that learned through trial and error. They stayed with the discomfort long enough for it to show them something.
Where will you be six months from now?
By then, your organization’s AI-first mandate may have set into motion departmental initiatives, vendor contracts and perhaps even some latest hires with “AI” of their titles. The dashboards shall be green, and the board deck may have a complete slide on AI.
But within the quiet spaces where your actual work happens, what’s going to have meaningfully modified?
Perhaps you'll be just like the teams that never stopped their quiet experiments. Your customer feedback system might catch the patterns humans miss. Your documentation might update itself. Chances are high, for those who were constructing before the mandate, you’ll be constructing after it fades.
That’s invisible architecture of real progress: Patient, and completely tired of performance. It doesn't make for excellent LinkedIn posts, and it resists grand narratives. Nevertheless it transforms firms in ways that really last.
Every organization is standing at the identical crossroads at once: Appear like you’re innovating, or create a culture that fosters real innovation.
The pressure to perform innovation is real, and it’s growing. Most firms will give in and join the theater. But some understand that curiosity can’t be forced, and progress can’t be performed. Because real transformation happens when nobody’s watching, within the hands of the people still experimenting, still learning. That’s where the longer term begins.
Siqi Chen is co-founder and CEO of Runway.
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