How AI Lets Developers Tackle Unfamiliar Challenges with Confidence

AI coding assistants are transforming software development by making complex tasks achievable in hours instead of weeks. Developers can now build APIs, automate workflows, and experiment boldly thanks to AI's ability to accelerate coding and reduce barriers to unfamiliar technologies. 

This shift isn't just about speed; it's about confidence. By removing repetitive work and simplifying intricate processes, AI enables teams to deliver ambitious solutions under tight deadlines. In this article, we'll explore how one team leveraged AI to turn a last-minute data integration challenge into an opportunity for innovation and what that capability might mean for the future of development. 

The problem: Digitizing static attendee data at the last possible moment 

A few years ago, the mobile team I led developed a feature to digitize PDF documents of attendee and company profiles for events and make them available within the WWT mobile app. This document is several hundred pages long, changes frequently and is updated almost up until the event, serving as the source of truth for this information. Because updates would happen very close to the event date, we needed a way to integrate data quickly once finalized. 

First steps: Using AI to bridge the gap 

To migrate data fast, we used an early version of WWT's generative AI assistant, Atom Ai. This allowed us to process the data securely within our own environment and convert the PDF into a large JSON document. That JSON was then stored and retrieved by the application as needed. Because the JSON payload was large, fetching it was slower than an optimized alternative, but it was accessed infrequently, making the tradeoff acceptable. We used this process for a few years, and it worked well with minimal upkeep. 

Revisiting the problem by building a more solid solution with the help of an AI coding assistant 

Recently, we had an upcoming event where the stakeholder group requested that attendee data be included in the app again. When we met to discuss what was needed, we learned that they no longer required a PDF, as attendees were accustomed to using the app at this point. They preferred giving us the data in spreadsheets, which opened the door to a better process. This seemed like the perfect opportunity to improve the user experience by migrating the interface into a fully featured API. By fetching each attendee's data individually, the app could more quickly and reliably access each attendee's information, even in areas with poor connectivity. 

We would have never considered this in the past because even the most basic APIs can be time-consuming to build. However, with the help of an AI coding assistant, we were able to quickly build an API that allowed users to sync their spreadsheet directly to the server. Something that at one point would have been deemed out of scope became doable in hours, not weeks. Due to the time savings, we had the confidence to be more aggressive with our solution. 

Good software development practices continue to matter 

AI coding assistants can feel like magic, but they're not infallible. We learned this when some untested aspects of the API, written by the AI coding assistant, nearly made it to production, but thankfully, we caught it in time. 

AI Assistants can be overconfident. Capable, but not always aware of the wider context. Overall, building this feature was much quicker than it would have been, but this experience reinforced that as we move into this new development world, the nature of our work is going to change, and we'll need to adapt our processes to meet it. 

Using our new powers to experiment boldly 

Although the app was now the preferred way to view data, we wondered if having a PDF of this data would still be valuable if the amount of work to create it was negligible. On a Friday afternoon, our team experimented: could AI generate a PDF rivaling the manually created version? A few prompts later, we had a proof of concept. The generated PDFs contained all the data and organization of the originals but were created in under 15 seconds with working links and custom branding. My team – which is primarily a mobile team – was able to build a working PDF using only web technologies in no time at all, without needing to install any plugins or learn new frameworks. What started as a 'what if' became a working solution in no time, something we'd never tried before. 

As AI gains ubiquity, as always, culture is our safety net 

This was the largest feature on which our team relied heavily on AI coding assistants, and, as before, after the initial prototype was built, we needed to make some changes. Team code reviews and shared standards become even more important with AI coding assistants because it's easy for a single human developer to miss things. One of the lessons we learned is that when you're on a team with others and asking them to code-review something that you don't even understand, it can waste your team's time and undermine their trust. 

Coding assistants tend to introduce more duplication than a good developer would, which increases the risk of bugs. The biggest weapon against missed requirements, strange user experiences, and unmaintainable code, as we inevitably move toward AI-assisted coding being the norm, is a good culture where a team is staffed with people of diverse experience levels who can be trusted to work together and adapt to the tools and processes that work best for them.

Lessons learned and takeaways 

Reflecting on this journey, AI has reshaped not just our technical workflows but how we approach challenges as a team. From PDFs to APIs to automated processes, each step taught us about efficiency, adaptability, and collaboration. The real lesson isn't just about faster development - it's about staying curious, embracing change and finding better ways to serve users. 

Looking back, it's remarkable how quickly the boundaries of what's possible have shifted. Tasks that once seemed daunting are now minutes away, not because we've mastered every skill, but because we're willing to experiment, adapt, and trust new tools. AI isn't just changing how we code - it's changing how we think about what's possible. That's the real transformation: the ability and confidence to be ambitious.