Before I get into it, I want you to wear one of two hats while reading this:
If you’re a builder, an individual contributor who ships things every day, think about how you can influence your leadership so that the people, products, and company around you are more AI ready.
From my time leading teams Google deepmind, metaAnd Google Labs, I can tell you that the ICs that are winning right now are the ones doing this really well.
So my hope is that you’ll walk away with a clear picture of how to become that 10x engineer or that IC that really shapes their company’s strategy.
If you sit at the strategy level, I want you to think about how your organization is grappling with the questions I’ve raised.
These are the same questions I grappled with at DeepMind and Google, and the same questions I’m now helping companies work on externally.
How to Prepare for Physical AI: 5 Steps for Engineers and Tech Leaders
Jensen Huang called it “the chatGPT moment for robotics”. Deloitte says 80% of businesses plan to use physical AI within two years. Here’s what you really need to know and do to prepare…
Three pillars of AI readiness
I think of being ready for AI as three big things working together.
The first is business itself. And I use “business” loosely here; It really means your product and your technology. Is business AI ready or not?
The other team is: Are the people in your organization really ready for AI?
The third one is what most people miss. The point is whether you have a flywheel, a self-improvement system that keeps improving both your team and your product.
Let’s go through them.
Pillar One: Is Your Business AI Ready?
AI traffic has increased by about 7 x 10³ in the last two or three years. This is a huge change in the way people interact with the Internet.
Take a piece of that growth. ChatGPT receives approximately 2 billion queries every day. This is just ChatGPT, based on what is publicly available. About 150 million of these queries come from users who want to buy something.
I should have hidden this statistic and left you to guess: How many of those queries actually result in a transaction?
Less than 1%.
Think about what’s happening here. I go to ChatGPT with the broken sync, upload a photo and ask how to fix it. I act on the suggestions and it becomes very clear that I am not fixing it myself.
The natural next step is to find a plumber. I would usually go to Angie or Thumbtack. If I try to do this inside ChatGPT, I fail miserably.
So we have these agents sitting on a goldmine of intent data, capable of powering some of the best service commerce out there, and we convert less than 1% of it into actual business outcomes.
This pattern is visible in every product category. The same fundamental thing is being missed in the 40 or so products I’ve worked on at Google and Meta.
Solutions built before 2024 were designed for a human who would open a browser, create an account, and click through to a flow. After 2024, your customer is often no longer human.
It’s an agent acting on behalf of a human, and that agent fails when it has to log in, enter card details, or execute anything meaningful.
If your product wants to be AI readyIt has to be ready for the agent.
You’re going to be one of two companies in the next few years. You’re either competing for 30% more conversions and potentially increasing your revenue by 200x, or you’re still optimizing for a buyer journey that’s quietly disappearing.
Innodata GenAI Summit London: May 21, 2026
On May 21, the Innodata GenAI Summit is held in London for a day of rigorous, practitioner-led exchange on the challenges that will define frontier AI in 2026. Here’s what’s on the agenda, who’s in the room, and why it’s important for AI professionals…
A simple framework: discover, experience, transact
When I work with companies on this, I break down the AI-ready product question into three areas.
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Experience. Once an agent or user finds you, is the experience essentially AI? Many of us still come across websites that ask us to fill out five lines of form fields. He is a relic. The experience should be conversational where it makes sense, dynamic where it should be, and driven by context-appropriate recommendations. Rethink the product experience.
Exchange. Once they’re both working, think about the business level. Dynamic pricing based on geography, dynamic UI based on users churn, AI-powered funnel analysis. View the entire funnel through an AI lens.
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Pillar Two: Is Your Team AI Ready?
No product work matters if your team can’t think in AI.
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This is where I think the industry story is a little wrong. Everyone’s focus is on whether companies are adopt Ai. About 88% of companies use ChatGPT or something equivalent to it every day. But only 5% are seeing meaningful benefits.
