AI RECOMMENDATION VS HUMAN JUDGEMENT - PT2. Can we detect, assess, train for and hire for 'human judgement'?   Following our amazing conversation a month ago, we're back with Pt2 of our conversation on AI Recommendation vs Human Judgement. This time, we're going to examine whether it is possible to hire for human judgement, whether this should be a quality we actively look for and cultivate in existing employees?   • Defining the Undefinable – Breaking down "judgment" into observable, assessable components - pattern recognition, ethical reasoning, contextual awareness, and decisive action under uncertainty - that separate humans from sophisticated autocomplete   • The Automation Paradox – How over-reliance on AI recommendations actually atrophies human judgment skills in your workforce, creating dangerous dependency loops where no one questions the machine   • Detecting Algorithmic Deference – Identifying the warning signs in candidate behavior and employee decision-making that reveal someone who executes recommendations versus someone who evaluates them   • Assessment Science for Judgment – Moving beyond situational judgment tests to immersive, real-time simulations that reveal how candidates actually react when AI advice conflicts with observable reality   • Training Judgment in an AI-Saturated World – Practical frameworks for developing judgment capabilities in employees who've never made decisions without a digital safety net—and why this is now a retention imperative   • The Hiring Manager's Dilemma – When your own recruitment process is AI-driven, how do you spot candidates with superior human judgment? Breaking the recursive trap of algorithms hiring for algorithmic compliance   • Ethical Override Readiness – Case studies of catastrophic AI failures in hiring, finance, and healthcare—and the specific judgment capabilities that could have prevented them   • Cognitive Diversity vs. AI Consensus – Why homogenized "best practice" recommendations produce blind spots, and how to build teams whose judgment complements rather than mirrors algorithmic output   • The Speed vs. Deliberation Spectrum – Redesigning workflows and roles to protect space for human judgment without sacrificing the efficiency gains AI legitimately delivers   • Future-Proofing Leadership Pipelines – Why succession planning must now explicitly screen for judgment autonomy—and how to identify high-potentials who won't become mere executors of AI strategy     We're on Friday May 8th, 2pm BST. Save your seat (click on the green button) to register and follow the channel here (recommended) to be notified whenever we go live with a new show.   Ep379 is sponsored by Greenhouse   What do high-performing TA teams look like in 2026? Greenhouse analyzed data from over 6,000 companies and over 640M applications over the last 3 years to understand how recruiters are really doing and how hiring teams can keep up with industry standards.   Check out their new benchmarking report, The Hire Standard.