It took the internet 13 years to reach 800 million users. ChatGPT broke that number in less than three years. By February 2026, OpenAI announced it had over 900 million weekly active users.
According to a Gallup poll, in Q4 of 2025, 38 percent of employees integrated AI technology to improve productivity, efficiency and quality. While we are actively learning, experimenting and integrating it into our work, expectations in 2026 are quickly shifting. It’s no longer about keeping up; it’s about positioning businesses to lead in this new, exponential era.
If we look back, every major leap in technology has come in waves.
The mainframe era gave us computing power.
The PC era put that power on every desk.
The internet connected the world.
The mobile era made that connection constant.
The cloud made it scalable.
And now we enter the AI era, a wave that’s not replacing what came before but instead amplifying all of it.
What’s unique about AI is that it builds on everything — the data from the internet, the scale of the cloud, the mobility of devices and the connectivity of networks. When applied in the right way, it’s transformational, not just for technology, but for how people and businesses operate.
This is a once-in-a-generation shift. We’re living through a fundamental change that’s going to reshape every part of business.
The key is to make sure you’re riding the wave, not watching it go by. The reason AI is improving so rapidly isn’t just clever algorithms; it’s the scale of the investment behind it. This is the new industrial revolution, but instead of factories turning raw materials into goods, these ones turn electrons into intelligence.
AI only works when we make it work
An AI model on its own is just potential — it can become a writer, a composer, a developer, even an accountant or bookkeeper — but only if we give it direction and purpose. Human intent is what turns that potential into something meaningful.
Anyone who works on a computer has tasks that can be automated, and in many businesses, labor is the highest cost by orders of magnitude. So, any increase in productivity or reallocation to higher-value work is critical.
Based on what I’ve seen across IT teams right now, here are five realities to unlocking AI’s exponential value:
- AI requires real engineering, not just prompt hacking. Good engineering practices still matter: tests, docs, CI/CD, clean code. The reality is that AI capability is jagged – while it is astonishing in some areas, it is completely unreliable in others. Teams need the judgment to know when to trust it and when to fallback.
- Create exposure opportunities, curiosity alone isn’t enough. Hackathons, workshops and internal showcases help teams engage with unfamiliar tools and ideas. But the ecosystem moves fast — without focus, teams burn time chasing every new release. It’s important to anchor on real problems.
- Fuel momentum by empowering the curious. Spotlight the experimenters and lead by example. Celebrate learning out loud. Curiosity is contagious. Harness it to create a culture of exploration and adoption.
- Review culture becomes critical when code is cheap. AI can generate code in seconds, but it can’t tell you if it’s secure, correct or maintainable. Invest in review practices. Validation is the new bottleneck.
- Tooling is not enough — you need ownership and trust. Without a focused team owning AI efforts, they fragment. This isn’t just software, it’s change management
Following “best practice” means lagging innovation
I’ve seen it before when the board says, ‘We need an AI plan.’ Someone is tasked with setting up a task force, running a few pilots, sprinkling some AI into a product and calling it innovation. That is not a strategy; that is reputation management. A “plan” is just doing things; a strategy is an advantage – it answers why and how to win.
There are two types of companies right now: Those trying to understand where the world is going and those waiting to copy whoever figures it out first. Most fall into the second. They call it “best practice” but it’s really just lagging innovation.
If you actually have a point of view, you don’t need to copy. You build first and everyone else reverse engineers you later.
Treat today’s AI like the early internet: The tools are immature, the hype is high, but the direction is obvious, so get moving. Leadership should shift from managing output to deciding what’s worth doing at all. After all, in five years, jobs won’t be about delivering output; they will be about defending judgment.
The right AI investments in your company will pay dividends if done right – the technology is already more capable than most people realize.
Those who survive this wave will not be those with the best plan; they will be those with the best learning culture and the ability to adapt. Your best people can’t explore if they’re buried in governance. Give teams protected time, safe spaces and explicit permission to break things.
You don’t become AI-First by saying you are. You get there by learning faster than your competitors. Everyone has access to the same state-of-the-art models, but how to best use them is the alpha. Everyone is saying ‘this is moving too fast’ but slow learning is still the biggest competitive risk.
If you design your teams and systems for control, AI will break them. If you design for learning and adaptability, AI will supercharge them. Rigid hierarchies will slow down intelligence – human or artificial. Hire people who think in systems workflows and outcomes, not just those who can prompt code – a budget line for AI infrastructure means nothing if your team doesn’t understand what to do with it.
AI is reshaping job descriptions
While I lead a 200-person tech team, we’ve seen incredible transformations across all roles at our company just this past year. Our product managers are shipping code changes to production to millions of users, our design team is building working prototypes in code, the commercialization group is building interactive apps to drive sales readiness, our researchers are building their own instruments and our support team is training AI and Intercom to deliver answers in seconds, not days like our competitors. We don’t just have a faster team; we have a fundamentally different kind of team.
The lesson I’d share to other product and engineering leaders: Stop thinking about AI as a productivity and efficiency tool. You need to think about it as something that reshapes what a role even is. If you lead a team, you should be using AI as heavily as the best people on it. Leaders who do not use these tools are making second-hand decisions in a firsthand revolution. This is the first technology that can actually teach you, not just make you faster. Get hands-on, invest your time and get curious. You cannot lead a transformation if you have only read about it.
The biggest risk isn’t AI replacing people, it’s your organization stopping them from evolving.
Strap in. This isn’t slowing down.
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