Katelin saw AI being used in a fintech start-up in 2022, so she was already aware of the possibilities when ChatGPT launched. She put that to good use at Dropbox, where she has built systems that take a project scope from half a day's work to twenty minutes, and freed herself up to actually listen in meetings rather than take notes.

She explains what context actually means in plain terms, why rolling out AI to a team is just as much work as building the tool in the first place, and where she draws the line on what AI gets to do.

The episode closes with a question Katelin gets asked a lot: if you could only focus on one area of AI right now, what would it be? Her answer is skills, the reusable instruction files that document your processes in a format AI can act on, which can be built without any technical knowledge.

In This Episode

• Katelin's introduction to AI before the ChatGPT era, and what working with an early agent taught her
• Why people with management experience tend to get better early results from AI
• Transcription, structured folders, and custom AI tools: how Katelin manages projects at Dropbox
• The difference between using AI as a search engine and using it as a system built around your work
• What context actually means, with examples that require no technical knowledge
• Why AI rollouts fail, and why individual pain points beat company-wide mandates
• Filtering LinkedIn noise: building a system that turns saved posts into original content
• What Katelin will not let AI do without her own voice in the draft first
• Where to start if you have not started yet
• Skills: reusable instruction files, portable across tools, no technical knowledge required

Chapters

• 00:00 Introduction to Katelin O'Shea
• 03:53 Before ChatGPT changed everything
• 08:29 Inside a project manager's AI workflow
• 20:23 Context isn't technical
• 27:20 Why rollouts fail
• 36:40 Filtering the noise
• 44:00 Keeping the human in the loop
• 48:48 How to get started
• 57:40 Listener question: where to focus

You can find Katelin at aithatworks.io, on YouTube at @aithatworks_kate, on TikTok at @aithatworks, and on LinkedIn at linkedin.com/in/katelinoshea

If you have a question you'd like us to pick up in a future episode, you can get in touch at frmdb.ly/pod

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

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

Chapters

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

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 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

Chapters:

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

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/pod

To find out more about Russ, visit theClick

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

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/pod

In this episode:

• Why AI often starts as a novelty and then disappoints
• What changes when you add context
• Moving from prompts to building real tools
• Building applications without traditional coding skills
• Context degradation, and why AI chats “forget”
• Designing around AI limits with apps and workflows
• A real example, building a meal planning app
• Choosing tools without chasing shiny objects
• AI as a non-judgemental thinking partner
• Listener question, structuring AI for business plans

Chapters

00:00 Introducing Dr Dan Maggs

05:33 From fun to functionality

12:24 Building solutions with AI

19:42 Working around context degradation

25:28 New tools and shiny objects

34:30 Using AI as a non-judgemental sounding board

38:56 AI is making customised products achievable

48:09 Listener question: business plans