When Building Costs Nothing

When Building Costs Nothing

Aloha Legends,

You all know the drill. Requirements gathering that stretches for months. Detailed Figma designs that users can't actually use. PowerPoint mockups of theoretical workflows. Approval committees that meet quarterly.

Welcome to enterprise "agile"—waterfall wearing a trendy hat.

This made sense when building software was expensive. Every decision had to be right the first time. Every feature needed sign-off. Every workflow required documentation.

But what happens when building costs approach zero?

Everything flips.

Most organizations are still running the old playbook: Plan → Design → Build → Handover → Hope. Months of preparation for software that might work, for users who might adopt it, solving problems that might still exist by the time you ship.

When a business analyst can generate working software in days, the methodology revolution isn't optional—it's inevitable.

New playbook: Build → Measure → Learn → Build Better

Skip the PowerPoint mockups. Build working prototypes. Skip the requirements documents. Deploy with real users. Skip the approval committees. Create learning loops.

The difference isn't just speed—it's quality of feedback. Traditional prototyping gives you opinions about concepts. AI-enabled prototyping gives you data about real usage.

Faster feedback loops (2 weeks vs 6 months). Higher quality insights (experience vs theory). Reduced risk (small experiments vs big bets). Capitalizable assets (working software vs documents).

Here's what's happening in organizations that get this: Digital teams can't be tourists in the business anymore. They can't build solutions FOR operators from the comfort of head office. They need to be WITH users, embedded in the daily grind of work, decisions, and customer calls.

The cultural shifts are already happening:

• Planning culture → Learning culture
• Documentation → Demonstration
• Approval gates → Learning gates
• Risk avoidance → Risk management

The organizations that crack this code don't start with platforms or strategies. They start with problems worth solving. They embed digital capability where value gets created, not where the org chart suggests it should live.

They build AI that works in the real world of equipment breakdowns, shift handovers, and urgent customer situations. Because that's where the gap between AI theory and daily reality actually gets bridged.

Next time someone starts talking about requirements gathering phases, ask them: "What if we just built it and learned from real usage instead?"

Their reaction will tell you whether you're working with the future—or still funding the past.