Every decision has a cost. And I am not talking only about the cost of building something — I am talking about the cost of being wrong.

In many companies, that cost is enormous. Not because people make bad calls more often, but because their software is rigid. Any change goes through a long process, involves several layers of approval, and depends on deployment windows that open once a month (or once a quarter). In that scenario, a wrong decision is not a setback: it is a problem the company will carry for months. And when the cost of being wrong is too high, something worse than being wrong happens — people stop deciding. Every choice turns into a meeting, every meeting turns into a committee, and the entire company slows down trying to protect itself from a mistake that might never have happened.

Making mistakes cheap

There is another way to look at this: instead of trying to eliminate mistakes, make them cheap.

At Softerize, this is one of our biggest competitive advantages. Updating our SaaS is easy and low-stress for us. That turns the cost of a decision into something very cheap. We can make a decision today, ship it to production quickly, watch how users react, and adjust if needed. Being wrong stops being a disaster and becomes just one more data point.

This speaks directly to that expression made famous by the lean startup movement: _fail fast_. The idea of the MVP — the minimum viable product — was never about shipping something sloppy. It was always about shortening the cycle between decision and learning. The faster you find out you were wrong, the cheaper the mistake was.

What is interesting is that this mindset tends to be associated with early-stage startups, as if it were a luxury for those with nothing to lose. Our experience shows the opposite: even as an established player, we keep that agility. We make decisions, update the product, get real feedback from real customers, and correct course quickly. A company's maturity does not have to come bundled with slowness — that is a trade-off many people accept without questioning.

AI changed the equation (again)

If decisions were already getting cheaper thanks to good engineering practices — continuous deployment, well-designed architecture, automated testing — artificial intelligence has just cut that cost by another order of magnitude.

The time between "having an idea" and "seeing the idea running in production" has never been shorter. AI tools speed up development, prototyping, and code review. What used to take weeks can now take days; what took days now takes hours. And that amplifies exactly the advantage of those who already have a culture of fast decisions: if your process lets you adjust quickly, AI multiplies that speed. If your process is rigid, AI just makes you wait faster in the approval queue.

In other words: AI does not solve the problem of the cost of decisions. It exposes who has already solved it and who has not.

A system is a living thing

I often say that a software system is a living thing. It is born, it grows, it evolves over time — and it evolves alongside the business and the people who use it.

And, like any living organism, it sometimes grows an appendix: features that made sense at a given moment, served a specific need, and later stopped being useful. There is nothing wrong with that. It is part of evolving. The problem is when the company cannot remove the appendix — when every feature that ever entered the system is condemned to stay there forever, piling up complexity, because the cost of touching it is prohibitive.

A healthy system is not one that never grew an appendix. It is one that can remove them when they show up. Adding, adjusting, removing: all of that should be a natural part of the software life cycle, not a traumatic event.

Cheap decisions mean more decisions

In the end, the cost of a decision defines a company's culture. When being wrong is expensive, deciding becomes an act of courage — and courage is a scarce resource. When being wrong is cheap, deciding becomes routine. People experiment more, learn faster, and the product evolves at a pace that rigid competitors simply cannot match.

The question left for whoever leads product or technology is not "how do we avoid wrong decisions?", but rather: how much does it cost, in your company, to find out a decision was wrong — and to fix it?

If the answer is "a lot", maybe the problem is not in the decisions. Maybe it is in the software.