Ben Wibberley

This session dives into the evolving world of player testing, analysing player data, and Quality Assurance, exploring how studios can choose the right testing methods to match their game’s genre, scale, and audience. The panellist will discuss their best practices for:

• Selecting effective testing methods by matching player testing approaches to the game’s needs and development stage.
• Integrating QA throughout development by using pre launch and post launch testing to improve stability and player experience.
• Strengthening long term quality by leveraging community involvement and building feedback loops that support continuous improvement for future games and/or live updates.

AI is quickly becoming part of the default game-dev toolchain—and at the same time, “AI in games” can trigger strong player reactions. In this fireside chat, we’ll share a practical, indie-studio view of where AI tools genuinely improve speed and alignment across production, where they introduce risk (quality consistency, creative ownership, and trust), and how teams can set clear lines so AI supports craft rather than replacing it. Expect real examples, pragmatic trade-offs, and lessons that apply even without a large team or dedicated AI engineers. The discussion will explore:

• Where AI helps most in practice across pre-production (placeholders, art and music inspiration packs, rough storyboards), production (pipeline momentum), and late-stage polish.
• Common failure modes (inconsistent quality, “generic” feel, rework loops) and how teams catch them early with lightweight review routines.
• Player perception and transparency: how to talk about AI use in a human way that protects trust, avoids unnecessary controversy, and stays consistent across marketing, localization and the shipped product.