Why you need to treat AI like the new guy, with Russ Henneberry скачать в хорошем качестве

Why you need to treat AI like the new guy, with Russ Henneberry

In this episode of The AIQUALISER Podcast, John Bennett talks with Russ Henneberry, co-author of Digital Marketing for Dummies, about why AI often frustrates us, and why structure and judgement matter more than prompts, tools, or model choice. Russ reflects on a career shaped by repeated reinvention, from early internet marketing through to content, SEO, and platform shifts such as Google, Facebook, and now AI. He positions AI not as a creative shortcut or a mysterious intelligence, but as a general-purpose system that behaves predictably once its true nature and limits are understood. A central idea in the conversation is the “new guy” analogy. When AI delivers generic, bloated, or inconsistent outputs, it is usually because it lacks context. Russ explains that most frustration with AI comes from treating it as if it already knows the job, rather than recognising that it needs onboarding just like any new team member. The discussion moves on to why clever prompting rarely compensates for weak intent, unclear scope, or missing structure, and why letting AI run in auto mode can quietly undermine human thinking. AI will almost always overproduce, and the real work happens in editing, cutting back, and deciding what matters. Russ also cautions against constantly switching tools in search of better results. Staying with a small number of systems allows understanding to build properly, while novelty keeps attention scattered. If you have a question you’d like us to pick up in a future episode, you can get in touch at https://frmdb.ly/pod To find out more about Russ, visit https://theClick.ai In This Episode Why AI often feels inconsistent or disappointing The “new guy” analogy, and what it explains about generic outputs Why structure matters more than prompts or model choice How auto mode can trade speed for judgement Why AI overproduces, and why editing is essential The risks of tool hopping versus going deep with a few systems Why responsibility and authorship do not disappear as AI improves Chapters 00:00 Introduction to Russ Henneberry 10:11 What's Surprising About AI? 14:42 Structuring AI for Effective Use 23:43 The Importance of Learning AI Deeply 36:28 Diving Deep into AI Tools 46:29 Structuring AI for Business Planning 57:34 Taking Responsibility for AI Outputs