Alex Hyett tried building an AI-powered habit tracker app with his wife, expecting it to take days, not a year. He found no existing app that met their needs, from local data storage to custom icons and flexible goal tracking. The reality? AI didn’t automate the work. It didn’t eliminate the complexity. It didn’t even simplify it.

What followed was a year of coding, debugging, and learning the hard way that AI app development isn’t about prompts and dreams. It’s about solving real problems with real tools. This is what happened when he tried to build something simple, and why it matters for anyone trying to turn AI ideas into working software.

Why Building an AI App Isn’t as Easy as You Think

Alex Hyett expected AI to automate app development, but after a year of work, he found the opposite. The promise of AI didn’t eliminate the need for coding, debugging, or design. It didn’t even simplify the process. His goal was simple: a habit tracker with local storage, custom icons, and flexible goal tracking. Yet no existing app met those needs. He tried Streaks, but it couldn’t handle a long list of habits or custom icons. AI didn’t build the app. He did. The lesson? AI app development isn’t about prompts and dreams. It’s about solving real problems with real tools, and that takes time.

The Real Requirements Behind a Simple App

Custom icons and local data storage

Existing apps like Streaks failed to meet the need for custom icons and local data storage. The ability to use gen-emojis from iOS 18 was a key requirement, as predefined icons didn’t fit specific habits. Local storage was also non-negotiable, as neither Alex nor his wife wanted data on third-party servers. This pushed the project toward a custom solution, as off-the-shelf apps didn’t support these basic needs.

Flexible goal setting and tracking

Goal tracking had to be flexible, daily, weekly, monthly, or no goal at all. This was important for tracking irregular events like headaches. Most apps forced users into rigid structures, making them unsuitable. The need for occurrence counting, rather than fixed repetitions, added another layer of complexity that wasn’t covered by existing tools.

Reminder customization and note-taking

Reminder rules had to be customizable, specific days, times, or intervals. Note-taking was also essential for logging details like workout routines or guitar practice speed. These features weren’t standard in habit-tracking apps, forcing the team to build from scratch. The result? A simple app became a complex project with no shortcuts.

Why AI Didn’t Cut the Development Time

AI tools didn’t handle edge cases

AI-generated code handled the basics but failed on edge cases. For example, setting goals with no defined frequency, like tracking headaches, required custom logic that AI couldn’t anticipate. Manual debugging and rework consumed weeks.

Integration with iOS 18 features was complex

Using gen-emojis from iOS 18 was a key requirement, but integrating them required deep knowledge of Apple’s APIs. AI tools didn’t provide the right hooks or documentation, forcing Alex to consult Apple’s developer site and spend hours testing compatibility.

Customization required deep technical expertise

Custom icons and local data storage weren’t supported by existing apps, but building them from scratch needed expertise in Swift and Core Data. AI didn’t replace the need for coding, it just made the learning curve steeper for non-experts.

The Hidden Costs of AI App Development

Learning curve with AI tools

AI tools promised to simplify development, but they introduced a steep learning curve. Alex Hyett found that while AI could generate code, understanding how to modify and extend it required time and effort. This meant investing hours into learning not just the tools, but also the underlying frameworks and APIs.

Debugging AI-generated code

AI-generated code often missed edge cases. For example, tracking habits with no defined frequency, like headaches, required custom logic that AI couldn’t anticipate. Debugging these issues consumed weeks of work, proving that AI doesn’t eliminate the need for manual testing and problem-solving.

Balancing automation with manual oversight

Automation helped with the basics but failed on complex features like iOS 18’s gen-emojis. Integrating these required deep knowledge of Apple’s APIs, which AI tools didn’t provide. This pushed Alex to consult Apple’s developer site and spend hours testing, showing that AI can’t replace human oversight in critical areas.

What AI Can and Can’t Do in App Development

AI aids in code generation, not replacement

AI can generate boilerplate code and help with basic functionality, but it can’t replace the need for a developer. Alex Hyett found that AI-generated code handled the basics but failed on edge cases, like tracking habits with no defined frequency, such as headaches. This required custom logic that AI couldn’t anticipate.

Manual review is still essential

AI tools don’t eliminate the need for manual review. In fact, they often require more of it. Alex spent weeks debugging AI-generated code that missed key requirements. The tools didn’t handle integration with iOS 18 features like gen-emojis, which required deep knowledge of Apple’s APIs and manual testing.

AI doesn’t handle user experience design

User experience design remains a human task. AI may help with layout suggestions or basic UI elements, but it can’t understand the nuances of user behavior or create a seamless, intuitive experience. Alex had to manually design the app’s interface to meet his wife’s needs, from long lists to custom icons, something no AI tool could do on its own.

What This Means for AI Transformation in Business

AI needs human oversight and customization

AI tools can generate code, but they can’t replace the need for human judgment. Alex Hyett found that AI-generated code missed key edge cases, like tracking habits with no defined frequency. Custom logic and oversight were required to make the app work as intended. This means AI implementation in business can’t be a set-and-forget process, it requires active management and tuning.

Expect longer timelines for complex AI projects

AI app development isn’t a quick fix. Hyett’s project took a year, not because of AI’s limitations, but because of the complexity of real-world requirements. Operations leaders should expect similar timelines for AI implementation in manufacturing or quality control. The hype around AI often masks the reality of long, iterative development cycles.

Focus on value, not just automation

Automation is a benefit, but not the end goal. The real value comes from solving specific business problems. Hyett’s app wasn’t about AI for AI’s sake, it was about meeting real needs like local storage and custom icons. For business leaders, the focus should be on measurable outcomes, not just the promise of automation. AI works best when it’s aligned with clear, practical objectives.

The Road Ahead for AI and App Development

Future AI tools may reduce development time

As AI tools evolve, they will likely cut down on the time needed for basic app development. Tools like those Alex Hyett used in his project will become more capable of handling complex integrations, like iOS 18’s gen-emojis, without requiring deep API knowledge. However, this doesn’t eliminate the need for human oversight, especially when it comes to edge cases and custom logic.

User expectations will continue to grow

Users are no longer satisfied with generic solutions. They want apps that meet specific needs, like local data storage and flexible goal tracking. As seen in Hyett’s project, the demand for customization is rising, and developers must keep pace with these expectations without compromising on functionality or performance.

AI will become more of a co-creator, not a replacement

AI is not here to replace developers, it’s here to augment them. The future of app development lies in collaboration between AI and human expertise. Tools will assist with code generation, but the final product will still require thoughtful design, debugging, and user testing. This partnership will define the next era of AI app building.

Source: alexhyett.com