What makes an event system easy to use is that it’s efficient for organisers. Most are already time-poor—they don’t have time to read a thirty-page manual before they can get started. Things need to be obvious and close to where you’d expect to find them.

I’ve worked with systems where adding a stream meant looking under “categories”, or exporting attendees meant going into a separate reporting area. Once you know where it is, that’s fine, but getting there requires training. An intuitive interface and a well thought-out user experience make it easier for organisers to get started.

I agree that powerful systems need structure, but give people somewhere to start. Don’t open a blank canvas and expect them to know how to put everything together.

I’m a big believer in simple templates that draw on my experience as an event organiser. Most events share some common elements, so we can have those ready instead of asking people to build them from scratch.

That gives organisers a base they can get going with straight away. They don’t need to understand the whole system upfront—just how to add or change what their event needs.

Using AI has allowed me to build things I didn’t think were possible, especially in the time frame. But I still needed to go back and adjust what it built to fit how people would actually use it.

My events experience helps me shape what’s needed and how the pieces fit together. Speed is definitely an advantage of AI, but it doesn’t remove the thinking behind good design.

Exactly. That’s why testing with real people matters—seeing what they look for and how they interact with the system.

Good design requires a relentless back-and-forth: building, questioning, adjusting and testing. AI helps me get there faster, but that process is still essential.