Industry plays by different rules. Processes, metrics, and monitoring are designed to deliver predictable quality. It should not matter who is on shift as long as the process works.
So why can't software follow suit? Over the last two decades, the field has wrapped itself in uncertainty. Agile evangelism has made it perfectly acceptable not to commit to price, or even to quality. "Requirements change, specs are hard". True, but not the whole truth.
Standardizing the craft
Today, monoliths are out of style. More organizations prefer smaller, swappable solutions that can actually be specified in advance and, dare I say, purchased as finished outcomes rather than by the hour.
The snag is that many dev shops still sell hours and sprints, not results. If you want results, you need an industrial mindset all the way from sales to project management to quality assurance.
Cue AI. It won't send developers to the unemployment line just yet, but the horse-vs-tractor comparison is hard to miss. Productivity rises and, more importantly, becomes predictable. AI does not eliminate every efficiency gap, but it is already narrowing them, and the trend is not slowing down.
As a larger share of coding shifts to AI, quality levels out as well. The optimist sees weaker teams pulled up; the pessimist fears the funeral of top-tier excellence. Reality probably lives somewhere in the middle. The key point is that we can finally monitor quality at industrial scale. Deeper automated testing and QA bring the predictability we have been missing.
We're not at full-blown industrial software production yet, but the direction is clear. At Fluentia, we've already turned the ignition: new tools built, processes laid down, even outcome-based pricing models.
