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The Difference Between AI-First vs. AI-Ready is More Important Than You Think
Charlene Li
February 11, 2026
Every week, I hear another CEO announce their company is going “AI-first."
It’s not the best approach, and here’s why:
AI-first is an acceptable framing for a technology company. But unless you're Google, OpenAI, or building AI as your core product, putting AI in the driver's seat is the wrong approach. The real question is whether you're putting technology or leadership in control of your transformation.
That choice determines whether you actually transform your organization or just thrash around in the AI muck.
Where "AI-First" Came From (And Why It Doesn't Fit Most Companies)
Google's Sundar Pichai coined the term "AI-first" back in 2016 to signal the shift from mobile-first to AI-first. For Google, this made perfect sense because AI is their product. But now I'm hearing banks, hospitals, manufacturers, and retailers all claiming to be "AI-first."
The problem is that you're importing Silicon Valley's technology priorities into contexts where they don't fit.
I get the appeal: In spring 2025, 46% of Y Combinator's cohort were AI agent companies, and 47% of AI-first companies have reached critical scale. If you're building AI products for customers, "AI-first" is great branding and positioning.
But when enterprise companies say "AI-first," they're often describing a cost-cutting strategy dressed up as innovation. It translates to: We're going to replace headcount, spend heavily on tech and lightly on people, and watch our compensation costs fall as machines do the work.
Language matters. Your employees are listening. Your customers are listening.
The Klarna Effect: When AI-First Goes Wrong
Let me give you a very public example of what happens when you lead with technology instead of strategy.
Klarna, the "buy now, pay later" leader, partnered with OpenAI in 2023, and their CEO, Sebastian Siemiatkowski, wanted to be, in his words, "OpenAI's favorite guinea pig." They announced they'd use an AI chatbot to do the work of 700 employees.
Query resolution times dropped from 11 minutes to 2 minutes. Revenue per employee jumped 73% year-over-year. The CEO even made an AI deepfake of himself to report financial results, "proving even a CEO can be replaced." He went on record saying, "AI can already do all of the jobs that we as humans do."
Fast forward to 2025. Customer complaints multiplied. The responses were generic, repetitive, and lacked nuance. AI couldn't handle complex issues requiring judgment. Customer satisfaction scores dropped sharply.
So what did Klarna do? They quietly started rehiring people. They experimented with remote customer service agents and a hybrid gig workforce. And now the CEO says, "It's important that customers always have a clear path to a human."
AI thought leader Gary Marcus dubbed this the "Klarna effect." It describes the arc of loudly declaring AI will replace humans, then quietly rehiring them.
What Klarna got wrong:
They started with technology, not strategy
They optimized for cost, not value
They measured efficiency, not outcomes
They forgot that customer trust is built by humans
AI-Ready: A Different Approach
Here's what separates AI-first from AI-ready:
Starting Point
🔴 AI-first starts with asking: How do we apply AI everywhere we can?
🟢 AI-ready starts with asking: What problems are we solving?
Focus
🔴 AI-first focuses on efficiency and cost reduction
🟢 AI-ready focuses on creating strategic value
People
🔴 AI-first aims to replace humans
🟢 AI-ready aims to augment humans
Adoption
🔴 AI-first prioritizes speed only
🟢 AI-ready prioritizes sustainable transformation
AI-ready means you've built an organization that can adapt at the speed AI evolves. The speed at which you adapt is what gives you a competitive advantage.
The AI Paradox
The truth is that 88% of organizations are actively using AI, but 78% report no material financial impact.
Recently, research found that companies crowdsourcing AI initiatives from the ground up and trying to mold them into a strategy rarely achieve transformation. They get lots of activity but not many outcomes.
If you jump to adoption without building organizational capacity to create value, you won't get the transformation you want.
The Right Sequence: People First, Then AI-Ready, Then AI-Applied
A people-first strategy addresses what people are afraid of and what they're hopeful about. It builds their skills and redesigns their work. The biggest barrier to AI adoption isn't technology; it's fear, resistance, and lack of psychological safety at all levels.
AI-ready means having the right culture, governance, strategy, roadmap, and training in place. High performers share common traits, including bold vision, redesigned workflows, and the ability to scale fast. Companies where more than 50% of employees are AI-fluent deploy AI across seven or more internal use cases on average.
AI applied is where AI creates strategic value. Think big with strategy, start small with lower-risk opportunities, then expand to scale more complex applications that transform core business functions. The defining capability? Moving quickly from experiments and pilots into everyday practice.
As my co-author, Dr. Katia Walsh, and I write about in our book "Winning with AI" (releasing in March), organizations that develop "superhumans"—people who use AI to accomplish organizational goals AND amplify uniquely human traits like empathy, intuition, judgment, and wisdom— are the organizations that win.
The Bottom Line
AI-ready is harder than AI-first. It requires all that hard organizational work. It's about culture, skills, and mindsets. You have to have those before you scale.
But that's exactly why it works. Speed is the new imperative, but speed without direction is just chaos. Get AI-ready first, then move as fast as you can.
💬 Your Turn
We threw out half of our book. When Katia and I started our book, we didn’t expect it to change so much. Here’s the behind-the-scenes look at what it was like to work on a book for over 18 months.
AI ROI isn’t measured in dollars spent and made. It’s more complicated—and more impactful than that.
What I Can't Stop Talking About
The Klarna effect is real, and it's happening across industries. Before you announce you're "AI first," ask yourself: Are you solving for cost or value? The answer matters more than you think.
Want to learn more about becoming AI-ready? My co-author, Katia Walsh, and I are publishing "Winning with AI: The 90-Day Blueprint for Success" in March. Sign up for updates at winningwithaibook.com..
My Upcoming Appearances/Travel
Feb 27-28: OrthoForum 2026, Keynote, Tampa, FL
Mar 12: Private event, Oklahoma City, OK
Mar 25: Private event, Las Vegas, NV
May 7: YPO Global Marketing Summit, Keynote, San Francisco, CA
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