The AIQUALISER Podcast: discover how people are really using AI

In this episode of The AIQUALISER Podcast, John Bennett talks with Victoria Westcott, who juggles many roles including producing films and managing a winery, about how she uses AI across very different businesses and why the one place she keeps it out is the work she cares about most.Victoria and her sister Jen make independent films. To do that without a studio or investor, they need income that does not take over their lives. AI makes it possible to run a cleaning company, a landscaping business, a YouTube coaching channel and a side-line in wordsearch books, creating income without any of them becoming 'a job'.Victoria shares where she finds AI most useful but also where it does not work. Film budgets have defeated it despite a lot of trying: union rates, location-specific tax credits, and constantly shifting figures are more than it can reliably handle. Writing convincingly in a specific author's voice is similarly out of reach. Her most unexpected use case is something different altogether: a personal GPT built around the twelve-week year methodology, which plans her daily meals based on her schedule, fridge contents, and protein targets. The episode closes with advice for creative people uncertain about where AI belongs in their work. Victoria's answer: use it for everything outside your zone of genius and keep it away from the work only you can do. In This Episode- Victoria's route from inner-city teaching to independent filmmaking- How the AI helps Victoria create businesses to fund the films- AI and the Toronto International Film Festival: researching and personalising at scale- Different approaches across film, cleaning, YouTube, and word search books- Negative scaffolding: what it is, why AI does it, and how to remove it- Where AI falls short: film budgets and voice replication- The protein tracker GPT and why it works- Why most people using AI are making more work for themselves, not less- Zone of genius as a practical filter for every AI decision- Advice for creative people who want to use AI without losing their voiceChapters- 00:00 Introduction to Victoria Westcott- 07:44 AI for the mundane stuff- 17:34 Juggling roles with AI- 29:50 Negative scaffolding- 31:58 What AI can and can't do- 40:44 Protecting creative work- 52:10 Listener question: the zone of genius- 57:39 If AI disappeared tomorrow

In this episode of The AIQUALISER Podcast, John Bennett talks with James O'Regan, co-host of The Impact of AI Explored, about who is actually accountable when AI gets something wrong. James has been podcasting about AI since February 2024. His view of the technology is practical and consistent: useful, incremental, and nowhere near as groundbreaking as the hype suggests.The conversation moves through the hype that has failed to deliver, the security risks that get glossed over in the rush to try new things, and the guardrails question that James returns to throughout. Autonomous agents do not stop when something goes wrong. They keep going until told not to. That requires precise instructions, clean data, and documented processes. Most AI pilots skip all three. That is why most of them fail.The episode ends with a simple question: if AI disappeared tomorrow, what would James miss most? His answer is the efficiency. There are not things AI can do that humans cannot do, it just makes you quicker. In This Episode•       Two years of change: from experimentation to daily use•       The AI hardware that flopped, and what it says about hype•       Security risks in open-source agents and AI browsers•       Autonomous agents and the guardrails problem•       Why 70 percent of AI pilots fail•       What James will not hand over to AI, and why•       Talking to children about what is real•       Agents versus automation: how to tell the difference•       Custom instructions, sycophancy, and the AI relationship problem•       Listener question: keeping company data out of public AI systems•       If AI disappeared tomorrow: efficiency, not capability Chapters00:00 Introduction to James O'Regan03:35 Two years of talking about AI06:34 The biggest letdowns14:12 Cool but scary19:03 Staying in control32:04 Kids and AI40:12 Agents or automation?46:08 Day-to-day use and personalisation56:30 Listener question: blocking AI

Join John as he talks with Corinne Thomas, founder of Ethical Sales, about what responsible AI adoption actually looks like inside real organisations, and how to implement it without creating confusion, risk, or resistance.They discuss how AI adoption is usually driven by leadership, and why pressure to “move fast” often clashes with reality. Corinne shares what she sees when individuals respond very differently to AI, from enthusiasm to scepticism to outright fear, and why those reactions need to be handled deliberately rather than smoothed over.The conversation explores why the biggest risks often come from overconfidence rather than caution, and why slowing down can actually accelerate progress.They also dig into what helps people learn AI properly, and the continued importance of face-to-face learning, even when the tools themselves are digital.The discussion also explores where AI is genuinely making a difference. Much of the value comes from unglamorous work, admin, proposals, funding applications, and internal processes, rather than the headline use cases people often fixate on. The episode returns repeatedly to the idea that AI works best when it supports structure, not when it replaces thinking.The episode closes with a listener question on using AI for prospecting, and why expecting it to act as a data source often leads to unreliable results. Corinne explains where AI fits in sales research, and where human judgement and proper data still matter.Visit the Ethical Sales website to sign up to Corinne's newsletter.In this episode:The different ways individuals react to AI, and why that mattersWhy moving too fast often creates more risk than valueThe problem of shadow AI and uncontrolled experimentationWhat effective AI learning actually looks like in practiceWhy face-to-face still plays a role in building capabilityWhere AI is quietly making the biggest differenceKeeping human judgement in charge as AI becomes more powerfulWhat AI can and can’t do in prospectingChapters:00:00 Introduction to Corinne Thomas05:40 Who drives the decision to use AI?09:01 The three approaches to AI17:05 Why face-to-face still matters20:24 The risk of going too fast25:17 The beauty and challenge of AI progress30:02 Building AI capability35:42 Where AI is actually making a difference45:35 "I'm the human here"51:50 What AI can and can’t do in prospecting

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 frmdb.ly/podTo find out more about Russ, visit theClickIn This EpisodeWhy AI often feels inconsistent or disappointingThe “new guy” analogy, and what it explains about generic outputsWhy structure matters more than prompts or model choiceHow auto mode can trade speed for judgementWhy AI overproduces, and why editing is essentialThe risks of tool hopping versus going deep with a few systemsWhy responsibility and authorship do not disappear as AI improvesChapters00:00 Introduction to Russ Henneberry10:11 What's Surprising About AI?14:42 Structuring AI for Effective Use23:43 The Importance of Learning AI Deeply36:28 Diving Deep into AI Tools46:29 Structuring AI for Business Planning57:34 Taking Responsibility for AI Outputs

Many people try AI, enjoy it briefly, then struggle to make it genuinely useful. In this episode, John Bennett talks with Dr Dan Maggs about the shift from experimenting with AI to building practical tools, and what that makes possible for non-technical founders.They discuss how AI has moved from novelty to something you can actually build with, why context degradation causes long AI chats to break down, and how working with projects and workflows helps address those limits.Dan shares his own journey, from early experimentation to developing a working meal planning app, despite having no formal coding background. The conversation also looks at choosing AI tools without chasing every new release, using AI as a non-judgemental sounding board, and what this shift means for people who want to build personalised products.The episode closes with a listener question on structuring AI for complex tasks like business plans, and why thinking in terms of projects matters more than writing ever-longer prompts.If you have a question you’d like us to pick up in a future episode, you can get in touch at frmdb.ly/podIn this episode:Why AI often starts as a novelty and then disappointsWhat changes when you add contextMoving from prompts to building real toolsBuilding applications without traditional coding skillsContext degradation, and why AI chats “forget”Designing around AI limits with apps and workflowsA real example, building a meal planning appChoosing tools without chasing shiny objectsAI as a non-judgemental thinking partnerListener question, structuring AI for business plansChapters00:00 Introducing Dr Dan Maggs05:33 From fun to functionality12:24 Building solutions with AI19:42 Working around context degradation25:28 New tools and shiny objects34:30 Using AI as a non-judgemental sounding board38:56 AI is making customised products achievable48:09 Listener question: business plans