Over the past few months, we worked on something that started as an experiment and ended up transforming how our client creates content. For an international scale-up, we built an AI Library that turned a well-defined content strategy into a system: efficient, scalable, and consistent. What started as a prompt here and there became a real engine. One we designed, tested, and kept sharpening alongside our partners at Say Hai, who specialize in embedding AI into daily workflows.

Here’s what we learned from making it real:

1. If you can design the process, you can AI it. A common misconception is that AI can turn chaos into order. The teams that truly benefit from AI are the ones that already understand how their work flows. If you can map your process, from inputs to reviews to outputs, you can teach AI how to support it. This project worked because we started there: structure first, tools second.
2. Use AI where it adds value. We didn’t try to automate everything. Some tasks, like quick visual tweaks or fast editing, were faster by hand. But for repetitive, high-volume, or consistency-heavy work, AI gave time back without cutting corners. The magic isn’t replacing people. It’s freeing them to focus on the work that actually needs their brain.
3. Prompting is briefing, for a machine. Prompts don’t work because you’re clever, they work because you’re clear. We started treating every prompt like we treat our content briefs: What’s the goal? Who’s it for? What tone fits? What’s the context? If you can’t answer that clearly, AI won’t either. Once we started writing prompts like we write content briefs, the quality jumped fast, and the results started feeling less “robotic” and more brand-aligned.
4. AI has modes. Use the right one. We saw a big shift when we stopped using ChatGPT like a one-size-fits-all assistant. Instant mode gave us fast ideas and outlines. Thinking mode helped with research and strategy. Pro mode delivered polished, structured content. What made the difference was understanding how each tool approaches a task, and adjusting along the way.
5. Start small. Scale smart. The temptation to go big on AI from day one is real. But trust us: pick three use cases where it can make a visible difference fast. We started there, and it built trust, rhythm, and proof of value. Once the first wins landed, scaling the system was natural. No overpromising, just momentum.
6. Set expectations and build guardrails. AI is great at doing what you ask, but only if you’re crystal clear. That’s why we built in review steps, clear guidelines, and fallback options. AI helps surface insights and shape content, but the team still makes the final call. Quality stays high because the process respects the need for human judgment.
7. Test and refine, constantly. We didn’t write one library and call it done. We tracked what worked, flagged what didn’t, and evolved the prompts. To 25+ refined ones, each sharper than the last. Every week the system got better, because we treated it like a living product. That’s how we made it sustainable.
8. One owner = more consistency. Multiple people using AI equals chaos unless someone owns the system. That’s why we made sure the AI Library had a single owner responsible for updates, structure, and consistency. It didn’t just keep things organized, it gave the team a way of working based on a solid and reliable system, built to grow with them.

AI doesn’t fix bad processes. It scales the ones you already have. When you bring structure, sharp prompts, and shared workflows together, AI stops being hype and starts being helpful. This wasn’t about chasing trends. It was about building a system that works, and keeps working.

Big thanks to Say Hai for being the tech side of the puzzle. They helped us connect clarity with capability, which is where AI really comes to life.

– Written by Bart Waterschoot, B2B Marketeer at Tech to Market.