Organiser voice AI & Innovation Stage Solo Session
AI & Innovation Stage sponsored by
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Data is supposed to unlock better decisions. But in most event organisations, there's a gap between the people asking questions and the people who can query the database. At Wildkind, we decided to close that gap using AI.
Over the past year, we've deployed a conversational analytics tool that lets non-technical team members ask questions of our data in plain English. No SQL. No waiting for the tech team. Just questions and answers.
That's the promise, anyway. The reality has been more interesting.
In this session, we'll share the full story: the setup investment, the wins that surprised us, the failures that taught us, and the things we'd do differently with hindsight. This isn't a polished case study—it's an honest debrief from someone still in the middle of the experiment.
If you're evaluating AI tools for your team, or just curious whether "self-serve analytics" is real or hype, this session will give you a grounded perspective. You'll leave with practical questions to ask vendors, realistic expectations for the journey, and a clearer sense of whether this path makes sense for your organisation.
Speaker
Nate John is Head of Technology at Wildkind, the UK festival company behind Camp Wildfire, Camp Kindling, and Winter Wildfire. With 15 years' experience spanning games, fintech, travel tech, and now live events, he brings a multidisciplinary lens to technology leadership—always asking how systems can serve the people using them, not just the people who built them.
Nate holds a PhD in Computer Science with a focus on Machine Learning, giving him both the technical depth to evaluate emerging AI tools and the pragmatism to know when the hype outpaces reality. His games industry background shapes his approach: technology should feel intuitive, not like overhead.
At Wildkind, Nate leads technology strategy across ticketing, e-commerce, operations, and data infrastructure for a lean team that punches above its weight. He's particularly focused on making data accessible to everyone—because the best insights come from the people closest to the work, not the people closest to the database.