The dominant narrative is simple: “If you don’t use AI, you’ll fall behind.”
It’s a convenient statement. Because it avoids the only question that actually matters:
In which specific processes can AI generate measurable efficiency in my business?
When you’re building or running a SaaS, a marketplace, or any real digital operation, AI stops being “inspirational” very quickly and becomes an operational concern. Either it moves concrete metrics, or it’s just noise.
The metrics don’t change:
- task delivery time
- recurring operational costs
- conversion, retention, service quality
If there is no clear before and after, you’re not using AI. You’re just adding complexity to a system that was probably fragile already.
How I position myself when I talk about AI
When I talk about AI, I’m not interested in feeding the hype. I’m interested in whether it holds up over time.
That’s why I never start with tools. I start with business architecture:
- real workflows
- roles and responsibilities
- bottlenecks
- decisions that currently consume time and attention
Only after that does it make sense to talk about models, automations, or agents.
The distinction is subtle, but critical:
- not isolated, clever automations
- but repeatable systems that keep working after the initial excitement wears off
If AI is useful, it must be embedded into existing processes.
When it’s used to build disconnected automation castles outside the core business, the outcome is almost always the same: more dependencies, more maintenance, less control.
No magic. Just less friction in day-to-day work.
From generic AI to process-driven AI
Take a typical headline:
“New AI model released, more powerful than ever at generating text and code.”
The superficial reaction is: “Great, now I can do even more things.”
My reaction is different. If the model becomes a commodity, the model itself is no longer the value.
The value is:
- how you integrate it into real workflows (CRM, ticketing, analytics, marketing, sales)
- how you standardize its output so it doesn’t become a new human review bottleneck
- how you turn that capability into a competitive advantage that’s hard to copy: process, data, positioning
I’m not interested in “playing” with the latest model. I’m interested in identifying two or three real bottlenecks I can actually remove.
Everything else is sterile experimentation.
The message behind my content
Every piece I publish is grounded in a few constants:
- an entrepreneur’s perspective, not a tool user’s
- focus on business architecture, not isolated features
- direct language, because people who manage projects, budgets, and teams don’t need decoration
When I come across AI news, I don’t reshare it. I dismantle it.
I bring it down to the operational level and ask:
How does this change the day-to-day reality of someone who has to make decisions and allocate resources?
If I can’t answer that, it’s not content, it’s just tech commentary.