There’s a high-energy interview with Howie Liu, co-founder and CEO of Airtable, on Lenny’s podcast: How we restructured Airtable’s entire org for AI.

It’s worth your time because it shows an early attempt at moving from pre-AI to AI-native. Howie is in the arena, experimenting, and candidly sharing what’s worked and what hasn’t. Airtable may be better positioned than most SaaS firms to take advantage of enterprise AI, but even with that advantage, the effort to re-architect its structure, processes, and culture is immense.

I’ll break down what Airtable is doing right and the steps they haven’t reached yet, so you can avoid the same blind spots as you transform your own business.

A quick primer: What is Airtable and why does AI-native matter?

At its core, Airtable is a web-based database with a spreadsheet interface. Its power lies in letting business teams manage and synchronize data without needing to know database architectures and programming languages. .

With the rise of “vibe coding”—using AI models to generate functional software from prompts—Airtable faces a fork in the road. Either its product gets disrupted, or it becomes part of the new AI development stack.

Howie Liu is betting hard on the latter. Airtable could become the essential plumbing for vibe coding—things like authentication, permissions, integrations, and workflows that every app needs but no one needs to reinvent. He didn’t say it outright, but it seems clear their long-range strategy is to move up the stack and position Airtable as the default corporate interface for building with AI.

That’s a good strategy. But strategy isn’t enough. You need the right structure.

The Six Rules of Structure (in plain English)

Here are the six rules from Organizational Physics that apply to any company. Think of them like laws of physics: you can work with them or struggle trying to fight against them.

  1. If the strategy or lifecycle stage changes, change the structure. A new game requires new alignment.
  2. Don’t let short-range functions control long-range ones. Urgent can’t smother important.
  3. Don’t let efficiency functions control effectiveness ones. Don’t let scale smother discovery.
  4. Don’t let centralized control overpower decentralized autonomy. Push decisions to the edges—except for what truly must be centralized.
  5. Put people (and agents) where they can focus and thrive. Strip away competing accountabilities.
  6. Process brings structure alive. The right cadence keeps the system adaptive.

What Airtable gets right

Rule #1: Change the structure when the strategy changes.

Howie saw that AI threatened Airtable’s model and acted. In the interview, he describes reorganizing teams multiple times to find the right fit. He’s right: there is no “one best structure.” Structure has to fit your strategy, your market, and your lifecycle stage.

Rule #2: Separate long-range from short-range.

Airtable divided development into “slow-thinking” and “fast-thinking” teams, inspired by Daniel Kahneman’s Thinking, Fast and Slow. That’s another way of saying: keep long-range work (architecture, positioning, future bets) separate from short-range work (iteration, shipping). Both matter, but they run on different clocks.

Rule #5: Put people where they can thrive.

Two insights stood out from the interview.

First, AI fluency. Howie is personally driving AI literacy across Airtable. His mantra: take time off to play with models and agents—learn by doing. He even says he’s the company’s biggest user of AI inference credits. If a Silicon Valley software firm on the cutting edge needs that kind of push, imagine your own company. Odds are, you have more AI laggards than Airtable. Raising fluency, which can only come from daily and hourly use of AI, will take real energy and investment, or you’ll need to find a workforce that is already AI fluent.

Second, the rise of the Individual Contributor (IC). Howie points out that AI enables leaders to return to hands-on work. With AI copilots, product managers can code, designers can analyze data, and engineers can experiment across domains. The line between “manager” and “maker” blurs. This also means roles should be rethought: leaders aren’t just coordinators—they can be high-leverage contributors again.

The lesson: putting people where they thrive in an AI-native company means they must not only be AI fluent, but the roles themselves will evolve. 

Where Airtable hasn’t gone far enough

Rule #3: Don’t let efficiency control effectiveness.

Splitting fast vs. slow teams is good. But there’s another lens: effectiveness vs. efficiency.

  • Efficiency means accuracy, scalability, repeatability, quality. (doing things in the right way)
  • Effectiveness means experimentation, discovery, and rapid iteration. (doing the right thing)

Put an exploratory AI pilot team under the core Software Engineering (SWE) function (whose job must include creating efficiency, control, and quality), and you’ll strangle it. 

A better pattern: create a small, protected pilot team that reports outside of core Software Engineering. Their charter is to test 10 things fast, kill 7, advance 3, and hand off 1 or 2 winners to Product Management to nail as a fully functioning product with the core Software Engineering team. 

This is why your intuition says “don’t bury a skunkworks inside core ops.” The structure makes the outcome inevitable. 

Listen, there are countless examples of how to assess which organizational functions—beyond just product, engineering, and design—should be structured for greater effectiveness or efficiency, oriented for the long- or short-term, and centralized or decentralized. I could write a book on it—and, in fact, I have.

To make your journey into enterprise AI strategies truly actionable, you must understand these trade-offs and the architectural framework behind smart structural choices. So do yourself a favor: grab a copy of Designed to Scale: How to Structure Your Company for Exponential Growth or watch the How to Change Your Structure Videos to get started.

Rule #4: Balance centralization with decentralization.

The purpose of structure is to push autonomy to where the work happens. But left alone, organizations drift toward bureaucracy and risk aversion as they age.

To get full advantage of enterprise AI, some things must be centralized so that everything else can decentralize.

  • Centralize your data architecture. One single source of truth with governed access.
  • Centralize your “AI brain” and knowledge graph. This is the context layer that holds entities, goals, relationships, and policies together.

Once you have shared and centralized context, you can safely decentralize decision-making to teams, agents, and copilots at the edge. Miss this, and you either collapse into chaos (too little centralization) or stagnation (too much).

Rule #6: Process brings structure alive.

Structure by itself doesn’t create results. What the right structure must unlock is stronger performance in four key process cycles (your company may have others, but you definitely have these four):

  • Customer Cycle: How we identify, engage, sell, retain, support, and grow customers.
  • Product Cycle: How we design, develop, deploy, and evolve products—while also sunsetting what no longer fits.
  • Employee Cycle: How we attract, hire, retain, support, develop, and when necessary, release talent to maintain a high-performing workforce.
  • Strategic Execution Cycle: How we set direction, align the team, and execute effectively as conditions change.

Think of these processes as the horizontal engine of the company. The vertical structure exists to make each cycle run better and faster, iteration after iteration.

One tip: don’t treat defining these cycles as a “one-and-done” project. They are never finished. Your job as CEO is to make sure the organization consistently improves its ability to run these cycles with more speed, clarity, and precision over time. Obviously, AI creates rich opportunities to find leverage in each of these core processes.

If you’re ready to think more deeply about these cycles in the context of your own company, I walk you through how to map and optimize them in my book, Designed to Scale.

The bigger lesson

It’s tempting to mimic Airtable: “They split into fast and slow teams. We should too.” Don’t do that. Copying tactics without understanding principles leads to fragility.

Instead, look at your company through the six rules. Which levers have you pulled? Which ones are you ignoring?

AI isn’t just a new set of tools. It’s a structural shift in how companies organize and execute. Get the structure right, and adoption accelerates. Get it wrong, and you amplify chaos not results.

A different path: Fostr AI case study

While Airtable is experimenting with becoming AI-native, other companies are rewiring their structure more directly. One example: a leading e-commerce coaching firm facing challenges that went beyond technology—their core issue was structural.

The client journey was fragmented across multiple tools: Kajabi for course delivery, Loom for video, Slack for discussion, and Airtable for tracking. Coaches spent hours responding to repetitive questions instead of providing strategic mentorship. Refunds climbed, often because clients felt lost in a disjointed experience with no visible progress. There was no single source of truth for onboarding, communication, or the overall client journey.

Rather than treating this as a tools problem, Fostr AI (FostrAI.com) approached it as a structural redesign.

They began by deploying a centralized AI brain that captured the company’s mission, strategy, structure, history, people, and goals. On top of that, they built a custom knowledge graph connecting Airtable, Slack, and Outlook. Every client record became a living system—linked to relevant resources, conversations, workflows, and progress metrics.

With this foundation in place, they introduced workflow automation to convert stray emails and requests into structured, trackable actions—eliminating inbox chaos. Finally, they launched a fully branded learning environment integrated with AI guidance, replacing the patchwork of third-party tools with a cohesive, owned platform.

The results were not incremental—they were transformational. Coaches saw a 50–60% reduction in repetitive student questions. That freed time for strategic mentorship. With visible progress reports tied to real outcomes, refund requests dropped. Core AI workflows went live in under 30 days—far faster than traditional LMS or CRM implementations, which often take multiple quarters. Client engagement climbed sharply, thanks to a unified platform instead of tool sprawl.

The deeper story isn’t just about efficiency—it’s about structural evolution. Coaches shifted from administrative triage to high-leverage guidance. Clients moved from a fragmented experience to a coherent ecosystem. And a centralized AI brain became the connective tissue for people, processes, and progress—turning the company into a truly AI-native organization.

The lesson: AI isn’t just a feature—it’s a structural shift. When designed well, it rewires roles, streamlines complexity, and creates leverage in ways no traditional system can match.

Your AI readiness checklist

As you reflect on the lessons in this article and consider different paths to becoming an AI-native company of your own, here’s my checklist for making the transition in a smarter sequence. Each bullet links to more detail—or reach out to me directly for CEO coaching: 

Summary

Airtable is half-right: they’ve started pulling the correct structural levers. But unless they (and you) also master the other half—balancing effectiveness with efficiency, centralizing shared context, and running the right process—you’ll stall.

Remember: structure and process drive behavior. Get the structure and key processes right, and AI becomes fuel. Get it wrong, and it just amplifies the noise.

📌 P.S.  Jason Baxter, co-founder & CEO of FostrAI.com, and I are launching a new Slack channel for CEOs who get it—leaders committed to transforming their organizations with true enterprise AI. If you haven’t already, DM me for an invite.