Why Your Inclusion Wins May Be Based on False Evidence
In this episode, Dr. Jonathan warns that diversity and inclusion successes can be based on circumstantial, or false, evidence when organizations mistake correlation for causation. He explains how indirect data—like a modest rise in women hires—can be misleading if other factors (new policies, market shifts, leadership changes) aren’t accounted for. He introduces a practical "causality check" using three lenses—interference, interaction, and interpretation—to help leaders isolate the true impact of their initiatives, emphasizing the need for high‑quality, multi‑source evidence.
Why Managing AI Is An Inclusion Problem
In this episode, Dr. Jonathan argues that managing AI should be framed as an inclusion problem rather than just a technology issue. He highlights how AI reshapes work, often replacing repetitive tasks, and notes that HR leaders are still using...
Why AI Is Being Used As An Excuse To Stop Investing In People
In this episode, Dr. Jonathan warns that many organizations are using AI as an excuse to stop investing in their workforce, treating it as an "escape hatch" to cut training, development, and support. He highlights three core issues: the relentless...
Why “We Have Copilot” Is Not A Modern HR Strategy
In this episode, Dr. Jonathan critiques the notion that simply having an AI "co‑pilot" constitutes a modern HR strategy. He highlights three core points: organizations often adopt AI to jump on the bandwagon without defining a clear business problem; AI...
Why AI Made Inclusion Everyone’s Problem
In this 7‑minute episode, Dr. Jonathan explains how AI has turned inclusion from a niche DEI issue into a universal workplace concern by automating hiring, promotion, and restructuring decisions that affect all employees without transparency. He cites recent mass layoffs...
Why Your AI Adoption Scorecard Is A False Proxy
In this 6‑minute episode, Dr. Jonathan warns that many companies are using AI adoption scorecards that measure tool usage rather than the quality of decisions AI enables. He explains the proxy problem—when a metric like hours logged or reports generated...
Why Your AI Hiring System Is Making Decisions You Can’t Defend
In this episode, Dr. Jonathan warns that AI‑driven hiring systems often embed invisible filters—like automatically rejecting candidates with high salary expectations—without the recruiter’s knowledge. He explains how these hidden rules degrade decision quality, rely on untested assumptions, and leave organizations...
How to Turn Stories Into Inclusive Evidence
In this episode, Dr. Jonathan explains how to transform anecdotal stories about inclusion into rigorous, evidence‑based insights. He distinguishes claims, assumptions, and hypotheses, urging listeners to treat anecdotes as testable starting points rather than conclusions. He introduces the Peacock method...
Why Diversity and Inclusion Gets Cut First in Budget Reviews
In this episode, Dr. Jonathan explains why diversity and inclusion (D&I) initiatives are often the first to be cut during budget reviews, highlighting that finance teams target work perceived as discretionary. He identifies three red flags: lack of measurable outcomes,...
Why Inclusion Fails Before Leaders Engage
The episode explores why diversity and inclusion (D&I) initiatives often collapse before leadership even engages, focusing on three core problems: misaligned incentives, the chicken‑and‑egg dilemma of proving value, and the lack of a commercial business case. It argues that risk‑averse...
Why Your Inclusion Journey May Look Better Than You Think
In this brief episode, Dr. Jonathan reflects on his own inclusion journey, emphasizing that progress often looks less glamorous than expected, illustrated by his lengthy viva and financial struggles. He highlights the importance of surrounding yourself with supportive people who...