At Game Changer London, Leaders Say AI Reshapes Trust

More than 350 leaders, researchers and builders attended the one-day second Game Changer London at the Royal Institution of Great Britain in London to examine how artificial intelligence is reshaping trust and decision-making across finance, retail, media and startups.

Dr. Vanja Ljevar opened the conference with the line, “It’s a Matter of Trust.” Organizers and speakers described trust in algorithmic systems as both technical and social infrastructure that supports deployment and adoption.

In a session on impact investing, Joseph Tenzin Oliver and Samir Beg Ceric, with Shirley Choo moderating, examined how returns are being rethought when AI measures and optimizes social outcomes alongside financial return.

A retail panel presented with Recommend and moderated by Dr. James Goulding featured Laura Belchier, Ivan Andabak and Ekaterina Doubinina discussing personalization at scale and where targeted recommendations approach behavioral control.

Speakers in a media session that included Patrick Fagan, Lea Karam and Meropi Kylika, with Dr. Terri Holloway moderating, outlined how AI-driven distribution is changing audience attention and emotional response patterns.

A session in partnership with SmartCat.io, moderated by Dr. Holloway and featuring James Pearce, Michal Karnibad, Alexander May and Filip Baturan, examined organizational readiness and argued many companies face harder challenges in AI transformation than they expect.

The luxury panel with Elaine Reffell, Jennifer Slater and Sarah Angold, moderated by Mike Popescu in partnership with Latenta®, discussed how algorithms influence desire and perceptions of value in luxury markets.

In the closing panel on startups, Jenny Zhou, Jo Living, Josh Robson and Vaida S., moderated by Dr. Holloway, highlighted a shift in competitive logic: panelists emphasized reading behavior in real time over rapid deployment alone.

Across sessions, participants raised operational questions such as how to measure trust, how to align incentives when models optimize multiple objectives, and how to avoid unintended behavioral steering. Panelists argued that addressing these issues will require both technical solutions and clearer social agreements about acceptable uses of algorithmic influence.