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 voice

Chapters

- 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.

• 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

00:00 Introduction to James O'Regan

03:35 Two years of talking about AI

06:34 The biggest letdowns

14:12 Cool but scary

19:03 Staying in control

32:04 Kids and AI

40:12 Agents or automation?

46:08 Day-to-day use and personalisation

56:30 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.

• The different ways individuals react to AI, and why that matters
• Why moving too fast often creates more risk than value
• The problem of shadow AI and uncontrolled experimentation
• What effective AI learning actually looks like in practice
• Why face-to-face still plays a role in building capability
• Where AI is quietly making the biggest difference
• Keeping human judgement in charge as AI becomes more powerful
• What AI can and can’t do in prospecting

00:00 Introduction to Corinne Thomas

05:40 Who drives the decision to use AI?

09:01 The three approaches to AI

17:05 Why face-to-face still matters

20:24 The risk of going too fast

25:17 The beauty and challenge of AI progress

30:02 Building AI capability

35:42 Where AI is actually making a difference

45:35 "I'm the human here"

51:50 What AI can and can’t do in prospecting