Everyone's talking about AI transformation, but the real battle isn't about technology—it's about knowing when to jump in and when to wait.

The FOMO Trap Is Real

Let's address the elephant in the room: the fear of missing out on AI is driving terrible business decisions. [00:20:35] "Is it worth it to burn yourself out trying to keep up with the latest technologies? Well, I don't think there's one right answer, but let's think about a few points."

Every week, there's a new AI tool launching. Every day, there's an update that promises to revolutionise everything. The pressure to be an early adopter is intense, but here's what nobody's talking about: most of this urgency is manufactured anxiety, not strategic necessity.

The reality is more nuanced than the hype suggests. [00:20:35] "How much is it relevant to you? Well, you can still wait out a little bit and you can control your fear of missing out and sleep tight. I think now if you are in the creative industry, if you're working with science, for example, or technology, it has probably been impacted a lot more."

Industry Impact Varies Dramatically

Not all industries are created equal when it comes to AI disruption. [00:20:35] "If you're fixing engines or if you're painting houses for a living or if you're cutting hairs. I don't think it has been impacted a lot." The transformative effects everyone's talking about are concentrated in specific sectors.

If you're in the creative industry, technology, or data-driven sectors, AI has already fundamentally changed your competitive landscape. [00:20:35] "If you're not moving with the market and with the trends, you will be most likely be left behind." For these industries, early adoption isn't just beneficial—it's survival.

But if you're in traditional service industries, manufacturing, or local businesses with limited digital footprints, the urgency is different. You have time to learn, observe, and make calculated decisions about when and how to integrate AI into your operations.

The First-Mover Advantage vs. The Second-Mover Advantage

Here's where strategy gets interesting. [00:20:35] "There is always going to be the first mover's advantage, which is if you see a gap in the market and you get there first and you do things right, you will become the reference there. We have OpenAI again with chatgpt. They were the first to go to the market and now they became the reference."

Being first can establish you as the market leader. OpenAI proved this. But there's another side to this coin: [00:20:35] "There is also the second mover's advantage, which is, which is someone who sees an industry, sees a leader or a first mover, understands what that leader or first mover hasn't been doing, right, Improves on it, and then goes to market as a better option to better serve the clients."

Facebook didn't invent social media—MySpace did. But Facebook perfected it. [00:20:35] "They were the first movers, but they improved on the first thing and the rest is history." Sometimes, watching the pioneers make mistakes and then building something better is the smarter play.

The Resource Reality Check

This brings us to the practical consideration that most AI evangelists ignore: resources. [00:20:35] "The third point is about how much resources you have. If you can afford to be, you know, always checking the trends, understanding how your industry is moving, I think that's always a great thing. But if you don't have the time, if you're stretched very, very thing, can you compromise something else?"

If you're running a small business, you're already juggling sales, operations, HR, and strategy. Adding "AI research and implementation" to that list might be the thing that breaks your focus on what's actually driving revenue today.

[00:20:35] "For most people the answer is not unless you're a researcher, unless you work in an environment that requires you being there, being the first, if you're a journalist, for example, or something like that. And most of it as well, I think is a little bit of fear of missing out."

The Accessibility Paradox

Here's what makes AI different from previous technological waves: the barrier to entry is remarkably low. [00:02:07] "You don't need someone extremely technical to use chatgpt and improve maybe ten percent of what they're doing on a daily basis." Anyone can sign up for ChatGPT and start experimenting immediately.

But this accessibility creates its own problems. [00:09:27] "It's very, very easy to create a product today if you know what you're doing. You don't need a lot of technical skills to code and within a couple of days you get a product which is up and running with payment gateway and you can take on clients and it's much, much easier to go to market today and have a very good product than it was just twelve months ago."

The result? A flood of AI-powered products and services that solve the same problems. The technology barrier has been eliminated, but the business fundamentals remain unchanged.

The Distribution Reality

This is where most AI adoption strategies fall apart. [00:09:27] "But there's one key thing about business which is acquiring customers. Selling is still key and that's true for the one man band who's starting today in their bedroom and is also true for the biggest players."

The proof is in the biggest AI success story: [00:09:27] "There is a reason why OpenAI has made agreements with both Microsoft and Apple and it's not because of the technology. They already have the best technology there is for their specific niche today. They are the benchmark of the market. So why are they still doing deals with Microsoft and Apple? Because of distribution."

Having the best AI-powered product means nothing if nobody knows about it. [00:09:27] "Anthropic, for example, which has arguably a product which is just as good, doesn't have as much distribution as OpenAI does. Therefore their market share is a lot, lot smaller."

The Noise Problem

AI adoption creates another challenge: information overload. [00:09:27] "AI will also create a lot of and generate a lot of noise. A lot of people just put their content machine on auto and there will be a lot of crap out there."

As AI tools become more accessible, more people will use them to create content, products, and services. The quality signal-to-noise ratio will decrease dramatically. Standing out will become harder, not easier.

[00:09:27] "So I think customer acquisition and branding will be the next big bet, the next big bets if you want to have a winner business." The companies that win won't necessarily have the best AI—they'll have the best brand and the strongest customer relationships.

The Strategic Framework

So how do you decide when to adopt AI? Use this framework:

1. Industry Impact Assessment: How much has AI already disrupted your sector? If your competitors are using AI to deliver better, faster, cheaper solutions, you're already behind.

2. Resource Evaluation: Can you afford to invest time and money in AI adoption without compromising your core business operations? If not, focus on what's working first.

3. Strategic Timing: Are you better positioned to be a first-mover or a second-mover in your market? Sometimes watching and learning from others' mistakes is more valuable than being first.

4. Distribution Strength: Do you have strong customer acquisition channels? If not, AI might be solving the wrong problem for you.

The Long Game

Remember, [00:02:07] "the problem is that it's really difficult to predict where it's going. If it has changed so much in the past two years, we definitely don't know what's going to happen in the next five years." The AI landscape will continue evolving rapidly.

The goal isn't to predict the future perfectly—it's to position yourself to adapt quickly when the time is right. [00:02:07] "All we can do is make sure that we keep investing on learning and development and that we don't allow ourselves to feel too comfortable."

AI adoption isn't a binary choice between being cutting-edge or being left behind. It's about making strategic decisions based on your specific situation, resources, and market position. The companies that thrive won't be the ones who adopt AI first—they'll be the ones who adopt it smartly.