Experiments led to increased productivity and reduced costs

In an era where digital transformation and AI integration are converging, the tools we choose can significantly impact our productivity. Powerful tools in the hands of skilled craftspeople can deliver impressive results. A recent project with a longtime client validates this mindset.

A pair of ITX Delivery teams had been working alongside their client counterparts for years; together, they’ve used Miro’s collaboration tool to support their joint activities. When ITX Product Owner Brandon Pierce discovered ways to leverage Miro’s AI Assistant, he started encouraging his teammates to play with it a bit to see if there might be room for it in their shared toolbox.

Initially, the client’s reluctance to bring AI technologies into their product development process constrained the team’s early work. That is, until Brandon’s experiments started to show real promise. Encouragement from these early-stage experiments proved to be the gentle nudge the client needed to explore Miro’s AI Assistance more thoroughly.

Alejandro Tommasino is a fixer. Well, he’s a Solutions Architect at ITX Corp. But when something’s not working properly and no one else can figure out why, you call in Ale.

That’s exactly what happened when ITX was deploying a new API to access a My SQL database. The codebase was supposed to go into multiple databases, create a report of ITX employees and their roles from the data, and then deliver that report. However, the site would always mysteriously crash about six hours after the report was delivered.

The team was stumped; a lack of error management in the code prevented them from figuring out what exactly was failing. If your car just shut down while you were driving without showing something like a check engine light, anything could be the problem.

So, that’s what Ale was tasked with. The problem? Python. Although Ale had experience with C#, PHP, and JAVA, the project was written in Python, a programming language that he had never even touched before.

Very early on, Ale decided that he would use ChatGPT to help diagnose and solve the problem; however, he realized the AI would be best used not as a magic box that spat out code, but as a learning tool. “It’s cool to say AI fixes everything, but when it comes to technical things, like coding, there are still a lot of errors,” Ale said. “When I moved my mindset to not having the AI do the work for me, but be there for me and enable me, that’s where I found it to be most helpful.”