# Bonus: Maximising AI Productivity

Page: https://stenobird.com/podcast/happier-at-work-leadership-culture-performance-629891/bonus-maximising-ai-productivity
Text version: https://stenobird.com/podcast/happier-at-work-leadership-culture-performance-629891/bonus-maximising-ai-productivity.md
Podcast: [Happier At Work: Leadership, Culture, Performance](https://stenobird.com/podcast/happier-at-work-leadership-culture-performance-629891)
Published: 2026-07-20T05:00:00+00:00
Episode link: https://happieratwork.ie/happier-at-work-podcast
Audio file: https://dts.podtrac.com/redirect.mp3/op3.dev/e/episodes.captivate.fm/episode/170238ac-3f9e-4f32-b2d0-7e7b1b339a37.mp3
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JSON: https://stenobird.com/v1/public/podcasts/happier-at-work-leadership-culture-performance-629891/episodes/bonus-maximising-ai-productivity
Duration seconds: 1008

## Resource

In this bonus episode, Aoife O'Brien breaks down the key insights from her conversation with Rebecca Hines about AI, productivity, and human judgment at work. If you haven't caught the full interview yet, this is your highlight reel, and a great reason to go back and listen to our chat. What we Cover The AI Productivity Paradox Why individual productivity gains from AI aren't translating into gains at the team, department, or organisational level. "Bot-sitting" vs. "Bot-shitting" Individuals report gaining back roughly 11 hours a week from AI tools, but a large chunk of that time goes into "bot-sitting": feeding context, cleaning up outputs, and re-prompting. When fatigue sets in, bot-sitting can tip into "bot-shitting", shipping unchecked, unverified AI output (aka AI slop). What Should AI Actually Be Used For? A look at the "jagged frontier" of AI capability, where it can excel at complex tasks yet stumble on simple ones, and why testing the boundaries (including AI immersion weeks) helps clarify where it genuinely adds value. Your "Secret Sauce" Why doubling down on what you're uniquely capable of, your human judgment, expertise, and critical thinking, matters more as AI takes on more of the routine work. Generalist vs. Specialist Why deep domain expertise, not broad generalist knowledge, is what lets you meaningfully challenge and question AI output. Blame and Responsibility The danger of outsourcing judgment (and blame) to AI, and why humans remain accountable for anything shipped, AI-assisted or not. The Context Problem Why generic AI tools lack your organization's unique context, and why it matters to actively challenge AI outputs rather than accepting the first answer, especially since AI is often designed to tell you what you want to hear. Defining "What Good…

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## Transcript

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