122% of Planned Value Delivered Using 97% of Budgeted Timeline

Until now, ITX research blogs have explored whether the integration of artificial intelligence might help our clients deliver high-quality software more efficiently by cutting costs and reducing time to value. Thanks to knowledge gained from those experiments, we have confidently leveraged new learning to the benefits of our clients and our teams. Research continues, but as shown in the post that follows AI integration into software development workflows is a bona fide difference maker.

A recent ITX project with a longstanding client stands as a powerful example of how AI tools in the hands of skilled developers can modernize an organization’s entire digital experience. Faced with a modest budget, tight timeline, and interactions with multiple SSO vendors, we partnered with our client to deliver a full website reskin, SSO integration, and significant security enhancements.

Why is documentation treated differently than the product?

Documentation is an often-underappreciated part of the products we create. Despite documentation’s important role in delivering a complete product, it may not receive the same priority in the minds of both clients and development teams. Why is documentation treated like a separate entity from the product itself, and what if teams integrated that product documentation into the development workflow instead? This question led to the creation of a new documentation process by technical writer Riona Macnamara and her team at Google. Macnamara’s team created a method for adding docs into a product’s development by adding documentation to the product on the code level, instead of treating docs like a low-priority add-on.

ITX slashes project timeline and cost by 50% to preserve client’s vital revenue stream.

This research post describes in detail how a second ITX project team – with an assist from AI – worked in parallel with their colleagues to revitalize a legacy platform modernization project. Through extensive experimentation and testing, the team used GitHub Copilot’s AI-assisted IDE (Integrated Development Environment) tool to complete 7 months’ worth of roadmap in fewer than 4 months.

Observations: The Client and Their Challenge

Our client is a non-profit organization committed to helping students plan for future success in post-secondary academics and beyond. They reached out to ITX to help modernize their aging online portal – a critical project. Failure to develop, test, and release the new platform in time for their annual selling cycle and busy onboarding period threatened a vital revenue stream.

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.”