A Culture of Immediate: Are We Getting AI Right, or Just Getting It Fast?
The tension between moving fast on AI and moving wisely: why speed alone won't deliver the transformation organizations actually need.
I spend my days in rooms where decisions are being made. Big decisions. Not the kind that simply move the numbers on a quarterly report, but the kind that shape the very experience that creates culture—and either grows it or changes the landscape of an organization and how it shows up in the world.
And when I say experience, I’m referring to a few points of view. There is the outward-in: the customer experience, the moments of trust, the brand promise customers feel, the entry point into an organization where human pressure testing takes place. And there is the inward-out: the internal experience within an organization—the people on the inside whose belief, energy, and ability to adjust determine whether the time, effort, and energy that went into making these big decisions actually lands.
Both of those experiences are being reshaped across industry verticals, and the question being asked isn’t whether or not to execute on AI—it’s about how fast it can be executed. And here is where I think a foundational challenge in AI exists, which I’ll come back to in a bit.
In the past few weeks, Coinbase announced a 14% reduction and described what comes next as “rebuilding Coinbase as an intelligence, with humans around the edge aligning it.” Now, when a company describes its future as “AI at the center, humans at the edge,” they mean it as a compliment to the technology. I understand that, truly. But customers don’t read the org charts. They feel the difference between a brand that backs self-service with humans and one that routes them to a model. Add Meta’s announcement of an 8,000-person reduction. The race toward IPOs by the major AI labs. The headlines are starting to look the same.
I have been thinking about a thread that runs through every report I have read on enterprise AI this month, and that brings me back to the foundational challenge I’m seeing in AI. Not the technology—the technology and what we are now doing with agentic AI is amazing. The pace has become the real challenge.
We are operating in a culture of immediate. We are the culture of immediate: immediate access to people, immediate access to information, immediate access to, well, everything. And this isn’t just our personal identity; it’s also our professional identity. Cost savings that show up in net earnings. Activist investors. AI hype cycles measured in weeks. Boards asking CEOs what they are doing about AI by next month. The pressure to act fast is not subtle.
And the most legible thing organizational leaders can do quickly is cut—cut headcount, cut layers, cut the cost of the people who do not directly touch the AI (for now). Implement the technology to account for the reduction in force and then publish the memo. Move the stock. Show the result on the bottom line. Let’s be clear: cutting headcount is not new in corporate America.
It is worth pausing and asking honestly: are we getting this right?
What’s Clearly Right
Some of what is happening is the right call. AI is real. Displacement is real. The companies that wait will fall behind. In life, we make bets. Be wrong about the AI bubble and save some money; be wrong about organizational displacement and lose permanently. The case for moving is strong.
And the prize is real on the other side of getting this AI thing right. I’ve long spoken with customers about creating the best friend experience. What I mean by this is: I can go weeks without talking to my best friend, but when we reconnect, I’m not reminding my person where I live, what my kids’ names are, or how many dogs I have (the answer: a lot). We just pick up where we left off. That is the experience customers want from the companies they do business with. They want connection. They want to be seen and heard, while also being easy to access, secure, and consistent across whatever device they happen to be on. AI is incredibly powerful at enabling experiences when it is designed and implemented correctly.
But moving is not the same as moving wisely.
What Gets Cracked First When Speed Is the Metric
The data on this is uncomfortable. KPMG’s Q1 2026 AI Pulse found that 67% of leaders cite employee job security concerns as a top barrier to AI adoption, 62% point to skills gaps as their top barrier to demonstrating AI ROI, and 87% say upskilling the existing workforce is their number one AI priority. You cannot widen the skills gap and close it at the same time. And reducing headcount is not just reducing people—it’s reducing people who hold institutional knowledge: the tribal understanding of what works in your specific operation, and the mid-career employees who connect departments and translate between functions. Those are the line items that are easiest to defend in a spreadsheet, but also the hardest to rebuild once they are gone.
Perhaps more concerning are the things you cannot see when moving too fast. I like to refer to this as the compounding cost of judgment debt: the moments when customer relationships built over time suffer, customer satisfaction drops, and employee morale is impacted—the signals leaders are sending into rooms they cannot see. I don’t believe AI is universally erasing tiers of labor. I am cautiously optimistic—and cautious is the operative word—because most of the data we are analyzing is still pre-agentic. Agentic AI only began seeing practical deployments last year.
What Customers Feel
Customer experience has been moving toward an AI-plus-human model for decades, and that path makes sense. I believe in it. I’ve built my career around helping customers do exactly this. Thinking back to the early contact center platforms I worked on, we were deploying a form of AI back in the ’90s. But until recently, those deployments all centered on the human agent. Deploying “humans around the edge” is more than an operating model—it is changing how business evolves, and doing so at a speed that makes the phrase “pace of change” feel outdated.
It is a model that changes what becomes acceptable as the norm. Cell phones changed what we considered acceptable customer service. AI is doing the same thing, faster. Point being: AI can solve problems quickly. Humans build the trust that determines whether your customer returns when the problem is harder than the model can handle.
A Different Way to Read the Same Evidence
This is not an argument against AI. The customers I work with who are leaning the hardest into AI are doing the most disciplined work I have seen on the human side. They are mapping layers where the human experience is irreplaceable—judgment, empathy, accountability, the moments that define the brand. They are redesigning the work, not just shrinking the org. They are treating AI as an operating model change, not a cost-out event. And they are smart to do so. The subsidized era of AI we were all introduced to is changing.
The question worth wrestling with is not whether to act. It is what we are optimizing for when we do—and how to get there.
If the answer is the quarterly number, we will keep cutting while telling ourselves we are transforming. If the answer is a better operation in three years—building a company our customers trust more, a business our competition cannot easily replicate—then deploying AI looks a little different: slower in some places, more deliberate, less press release, more design.
A culture of immediate rewards the visible move. Real transformation rewards the harder one.
The companies I’d bet on aren’t the ones who moved first (although, thank you for taking the shrapnel). They’re the ones who decided what they were optimizing for before they started downsizing whole organizations.
Are we getting it right? Or are we just getting it fast?
#ArtificialIntelligence #CustomerExperience #Leadership
