What if you could peek behind the curtain and see exactly how people are using AI at work right now? In this episode, we dive into an exclusive, unpublished study from Microsoft that does just that. Titled "Working with AI: Measuring the Occupational Implications of Generative AI," the paper analyzes 200,000 real, anonymized conversations between users and Microsoft Bing Copilot to uncover how AI is reshaping the workforce. This isn’t theoretical. This is actual, on-the-ground usage, rich with data, surprising insights, and implications for nearly every job you can imagine.

We explore what people are really asking AI to help with and what the AI is actually doing in response. From writing and research to coaching and advising, the results may surprise you, especially the fact that in 40 percent of cases, what users wanted and what AI did were completely different tasks. The study maps these interactions to job roles using the O*NET occupational database, producing an “AI applicability score” that highlights which professions are most and least exposed to AI capabilities today. Spoiler: knowledge workers, communicators, and information professionals should pay close attention.

Whether you’re a business leader, knowledge worker, or educator, this episode offers a grounded look at how generative AI is actually being used across different types of work. The findings show that AI’s current strengths lie in supporting tasks like writing, information gathering, and communication, while its direct performance is most visible in roles involving teaching, advising, or coaching. Physical and manual occupations remain less affected, for now, but even those show signs of interaction. By focusing on real-world data rather than predictions, the episode provides a more nuanced view of how AI is fitting into the workplace today.

In this episode, we explore the results of a major new global study from the University of Melbourne and KPMG titled Trust, Attitudes and Use of Artificial Intelligence: A Global Study 2025. Drawing on the views of more than 48,000 people across 47 countries, this research offers one of the most detailed snapshots to date of how AI is perceived, trusted, and used around the world. It examines differences between advanced and emerging economies, workplace adoption, student use in education, and the growing call for stronger governance.

The conversation unpacks why emerging economies are leading the way in AI trust and uptake, and why advanced economies are showing more scepticism. It highlights the gap between public confidence in AI’s technical ability and concerns about its safety and ethical use. You will hear about patterns in workplace behaviour, from productivity gains to policy breaches, and how students are using AI both to enhance learning and, in some cases, to bypass it. The episode also discusses the widespread demand for stronger AI regulation, especially to counter misinformation.

This discussion matters because it captures the reality of AI adoption beyond the headlines, showing both its opportunities and its risks. The findings reveal where trust is being built and where it is eroding, and why literacy, governance, and clear regulation are critical as adoption accelerates. Whether working in a business, leading a team, or studying in a university, understanding these trends can help in making informed decisions about how to engage with AI responsibly and effectively.

Think machines can predict your next move? Think again. In this episode, we dive into one of the most intriguing challenges at the crossroads of psychology, artificial intelligence, and business: can we truly predict what people will choose before they do? We explore the breakthrough research published in Nature Human Behaviour, spotlighting BEAST-GB, a revolutionary model that blends the best of behavioural science with cutting-edge machine learning. It’s not just another algorithm—it’s a new lens on how humans really make decisions.

Join us as we unpack why pure AI and raw data alone keep falling short in predicting real human choices. Discover how BEAST-GB leverages psychological insight, cognitive biases, and decades of behavioural research to outperform even the smartest deep learning models. You’ll also hear how these ideas are powering new tools like Accurment, designed to help marketers and decision-makers move beyond guesswork and gut instinct, transforming messy human data into clear, actionable strategy.

If you’re curious about the future of decision science, want to understand the secrets behind truly effective marketing, or just love uncovering what makes people tick, this episode is a must-listen. Hit play to find out why the smartest predictions don’t come from AI alone, but from a powerful partnership between machine learning and human understanding.