Over the past few months, I’ve been asked a version of the same question by founders across different stages:
How are you actually running companies with AI?
There’s an assumption built into that question: that doing this at scale must involve a complex system. A large stack of tools, multiple layers of automation, and a tightly engineered workflow connecting everything together.
In practice, it looks very different.
What I’ve found is that running a company with AI is less about expanding your stack and more about reducing it to what actually matters.
Where Most Founders Go Wrong
AI has dramatically lowered the barrier to building. Products that once required teams and months of effort can now be launched by a single person in days. That shift has brought in a new wave of founders, many of whom are highly capable and move quickly.
But the ease of building has introduced a different problem.
Instead of focusing on the business itself, many founders start investing their time in constructing systems around it. They experiment with tools, connect workflows, optimize dashboards, and continuously refine their setup in search of efficiency.
On the surface, it feels like progress.
But over time, this approach creates unnecessary complexity. The more tools you introduce, the more coordination they require. Instead of simplifying operations, the system begins to demand attention on its own.
What Running a Company Actually Involves
Across the companies I operate, the core requirements remain consistent regardless of industry or scale.
You need to make decisions with clarity. You need to execute consistently. You need to communicate effectively. And you need visibility into what is working and what isn’t.
Everything else tends to be secondary.
When you look at it this way, the idea of needing an extensive AI stack starts to lose its relevance. Most of the work in a company doesn’t come from tools. It comes from how clearly you understand what needs to be done and how efficiently you move on it.
Simplifying the Stack
My own approach evolved in response to this.
Like most founders, I initially explored a wide range of tools. Each one promised to improve speed, automate processes, or unlock some form of leverage. Individually, many of them were useful. Collectively, they made the system harder to manage.
Over time, I began removing anything that didn’t directly contribute to execution.
What remained was a much smaller, more focused setup. Each tool had a defined role. There was no overlap, no unnecessary integration, and no constant need to maintain the system.
This shift made a noticeable difference.
With fewer moving parts, decision-making became faster. Execution became more consistent. And most importantly, attention stayed on the business rather than the infrastructure around it.
The Role of AI in This System
AI plays an important role, but not in the way many founders expect.
It does not replace the need for clarity. It does not remove the responsibility of understanding your market or your users. What it does is accelerate the parts of the process that are already well-defined.
When used correctly, it helps you think faster, execute faster, and iterate faster.
But that only works if the underlying system is simple enough to support that speed.
If the system itself is complex, AI tends to amplify that complexity rather than resolve it.
A Practical Approach
For founders building with AI today, the priority should not be to assemble the most advanced stack available. It should be to build a system that is easy to operate and difficult to break.
That usually means working with fewer tools, not more.
It means being intentional about what you add and equally intentional about what you remove.
And it means designing your workflow around how decisions and execution actually happen, rather than around what a tool is capable of doing.
To make this easier, I’ve put together a breakdown of the exact approach I use to manage operations across multiple companies using a focused AI stack.
It’s not a comprehensive list of tools. It’s a practical system built around what is necessary and what consistently works.
If you’re building with AI and want a clearer way to structure your operations, this is a useful place to start:
