449 3 FRAMEWORKS I LOVE USE

If you're making inclusion decisions without a system for testing whether your assumptions are true, I break down the 3 frameworks I use with clients every week.In this episode we cover:How 4 evidence sources ground every client decisionWhy assertions without evidence deserve dismissalHow to separate claims from testable hypothesesIf you're finding that your inclusion decisions are built on claims nobody has tested, start here:https://realitycheck.elementofinclusion.com/

If AI makes your juniors better but leaders keep cutting them, your talent pipeline breaks down. Here I trace the mispricing no one is naming.In this episode we cover:How AI lifts novice performance more than expertsWhy polished output misleads leaders about junior valueWhy cutting entry-level roles starves future leadershipIf you're finding that your leaders are cutting the people AI improves the most, start here:https://xray.elementofinclusion.com/

If the inclusion projects you start keep quietly slipping away, your work never compounds. Here I unpack how to fix the leaks.In this episode we cover:Why every body of work quietly leaksHow correcting yourself protects what you builtWhy starting small builds the bigger arcIf you're finding that the inclusion work you start keeps slipping away before it sticks, start here:https://implementation.elementofinclusion.com/

If your team uses AI but nobody is building judgement, the gap is forming now. Here I break down three modes of AI use and why only one builds skill.In this episode we cover:How adoption metrics hide a growing capability gapWhy the most common AI mode builds nothingWhen polished output disguises missing judgementResources referenced in this episode:Dell'Acqua, F. et al. (2024). Cyborgs, Centaurs, and Self-Automators: The Three Modes of Human-GenAI Knowledge Work and Their Implications

If your organisation measures AI adoption by usage, the metric masks a deeper gap. Here I break down what the best AI organisations measure instead.In this episode we cover:Why AI competency requires evidence not usageHow unspoken assumptions shape your AI cultureWhy your future team won't be entirely humanIf you're finding that your AI work produces activity but no measurable outcomes, start here:https://realitycheck.elementofinclusion.com/

If your inclusion results rest on circumstantial evidence, you take credit you cannot defend. Here I explain how to look for causation.In this episode we cover:How circumstantial evidence masquerades as proofWhy good work can produce wrong conclusionsHow the 3 I's test causationIf you're finding that you can't prove your results actually belong to you, start here: https://realitycheck.elementofinclusion.com/