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AI Process Automation at Quilyx: Lessons from a Hands-On Weekend
At Quilyx, we regularly explore how AI process automation can improve internal workflows. Last weekend, we built and tested an automated flow designed to handle a large volume of daily input and produce a clear, structured summary. Something our team could actually work with.
The input in this case? Dozens, sometimes hundreds, of emails and structured updates that previously required hours of manual scanning, tagging, and sorting. Now, the core details appear each morning in one clean overview.
This wasn’t built to replace people. It was built to let people focus.
Why We Explored AI Process Automation
When your team has to make fast, well-informed decisions, clarity is everything. But clarity is hard to maintain when the signal is buried in volume.
We wanted to see how far we could take a low-code solution using Microsoft Power Automate and AI support. The goal was simple: reduce the time spent on mechanical triage so our professionals can spend more time acting on what matters.
Within two days, we had a working prototype. And it changed the way we looked at the task.
Where AI Process Automation Helped Us Work Smarter
The finished flow scans and extracts key information from incoming emails and updates, then structures it into a daily summary format. It flags known patterns, extracts role details or context when relevant, and outputs a compact version that helps the team focus.
This is exactly where AI process automation adds value. Repetitive sorting. Extracting basic signals from noisy data. Generating consistent summaries from inconsistent input.
Without automation, we would not be doing this. Not because we do not want to, but because no one has the time to do it consistently. Now we do.
What AI Automation Got Wrong and What We Fixed
Microsoft’s Power Automate platform got us part of the way. The low-code interface is solid. But tools like AI Builder and Copilot still leave much to be desired.
So we used our ChatGPT Team environment to write custom logic and refine steps. Even then, it was about 60 percent reliable. The rest required trial, error, debugging, and a fair bit of frustration.
Sometimes we were polishing results. Other times we were scraping with a virtual file to get a rough shape into something usable. The end result was worth it. But it did not come free.
AI Process Automation Needs Human Oversight
The moment you expect AI to solve something for you, it breaks. We do not believe in magic buttons. We believe in tools that save time and increase consistency, but only when they are used by people who know what they are doing.
This was a perfect example. AI helped process the flood of input. But knowing what to surface, how to structure it, and how to handle the edge cases still took real work.
And that brings us to another lesson.
How We Apply AI Process Automation Without Losing Trust
We do not test these tools because we are looking to replace anything. We test them because we want to understand what works, where it fits, and how to stay sharp as the landscape shifts.
AI process automation has real power. But only when it is applied with care, ownership, and boundaries. That is what we do at Quilyx. We build the structure that lets people do their best work.
If you are curious where these tools could support your internal processes, we are ready to share what we have learned.
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