In this episode of AI Experience, we look back at five articles that each shed light, in their own way, on the growing presence of artificial intelligence in our lives.

• What’s your role when AI can do your job in seconds?
• Is artificial intelligence turning the App Store into a digital landfill?
• The real AI revolution is happening far from Silicon Valley, in small businesses across Africa and Asia.
• Your sports betting app knows you're likely to lose before you even place your next bet.
• Your coworker uses ChatGPT to save ten minutes. How much time do you waste fixing their work?

These questions reveal how far AI reaches beyond labs and tech companies: it now shapes our choices, our leisure, our purchases, and even our most intimate spaces.

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What does “AI-first” actually mean when most employees barely use the tools they’ve been given?

In this episode of AI Experience, Julien Redelsperger welcomes Chris Dyer, keynote speaker, culture expert, and AI executive focused on helping organizations adopt AI effectively. You’ll discover why handing employees an AI license is not an adoption strategy, why companies often focus on technology before understanding what people actually need, and why resistance can simply mean that AI has become one more task on an already overloaded workload. AI Experience.

If your organization says it is AI-first, this conversation may make you question what that claim really means.

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In this episode of the AI Experience Book Club, Julien Redelsperger looks at AI-Powered Leadership, by Dave Silberman, Rich Maltzman, Loredana Abramo, and Vijay Kanabar, published in March 2025 by Addison-Wesley Professional.

The book explores how artificial intelligence is changing the role of leaders and managers as it becomes more involved in analysis, recommendations, and decision-making. It argues for an approach in which human capabilities and AI complement each other, with particular attention to judgment, critical thinking, data quality, and bias. In this episode, you’ll discover why an AI-generated analysis can be accurate without necessarily leading to a good decision, how to use AI to challenge your own assumptions, and why human accountability remains essential as these systems become more capable.

The episode also looks at some of the book’s limitations: what should you do when your experience conflicts with the algorithm? How should responsibility be divided between humans and machines? And what happens to productivity gains when AI becomes fully integrated into everyday work?

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