From Initial Frustration to Truly Integrating AI in Development: How We Transitioned from Hype to Concrete Results in Our Projects
The Year AI Stopped Being a Promise and Started Changing How We Work Midway through last year, the industry was talking about nothing else. AI was everywhere. Success stories, incredible metrics, the future of development. We, on the other hand, felt like we were doing things twice. The time we spent reviewing, correcting, and redoing what the AI generated exceeded the time we supposedly saved. Frustrating because the promise was real but the results were not arriving. Something changed in the second half of the year. The second semester brought models with a different level of capability. Tools like Claude and Codex— which we now use in our daily development—stopped feeling experimental and began to genuinely fit into the workflow. It wasn't magic; it was the combination of better models with learning to work with them. That’s when everything started to work. It wasn't an exact moment. It was a gradual adjustment in how we used the tools, what we asked of them, and how we integrated that into the actual workflow. Little by little, the numbers began to shift: today, 50% of the code in our projects is written by AI. Deliveries are quicker and more comprehensive. But the most interesting part is not the speed. It’s what we do with the freed-up time. A client asked us to generate a PDF with a work order, something specific and limited. We delivered it, and we also added a QR code so the mechanic could upload photos and videos directly from his phone, without installing anything. A simple requirement that came out with steroids. Before, that extra feature would have appeared only in the second or third stage. Now it enters in the first delivery because we have room to iterate faster and think beyond the requirement. That’s what changed: we not only deliver faster, we deliver better than the client expected. And this is not exclusive to software development. The challenge of integrating AI into a real workflow—what works, what doesn’t, and how to truly make a difference—is the same for management, finance, and logistics companies. We have already traveled this path. We know where the stumbling blocks are.
If your company wants to genuinely incorporate AI, not just experiment with it, let’s talk.
