In this episode of Beginner’s Guide to AI, Wendy Keir shares practical ways small business owners can use AI tools to save time, reduce decision fatigue, and build a “team” of custom GPT agents. From naming her CEO agent “Lucas” to a dead-simple rule — one GPT, one job — Wendy shows how entrepreneurs can turn AI into a reliable thinking partner for growth in 2025. 🚀

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💡 Key highlights

• Practical AI tools for small businesses: email drafting, planning, campaign support, weekly reviews

• Custom GPTs / agents: why one GPT, one job beats generic prompting

• AI productivity & time savings: ~7 hours/week saved; ~£1,000/week during campaigns

• Adoption mindset: staying in the driver’s seat; context > canned prompts

• Accessibility & inclusion: how AI levels the playing field for solopreneurs and small teams

• Beginner’s Guide to AI takeaways: concrete workflows any entrepreneur can start today

  
  

➡️ Quotes from the Episode

• “I don’t encourage anyone to prompt — I encourage them to create an agent that fulfills a specific role.”

• “One GPT, one job. You don’t want multiple personalities in one agent.”

• “AI levels the playing field for everybody; it meets you where you’re at.”

🧾 Chapters (experimental)
00:00 Welcome & intro to Wendy Keir
03:45 Why AI clicked for a dyslexic entrepreneur
08:30 From prompts to agents: one GPT, one job
14:20 Building a family of business agents (CEO, coach, marketing, sales)
20:15 Daily workflow with “Lucas” the CEO agent
27:40 Time and money saved with AI in campaigns
34:10 Overcoming resistance and starting small
40:00 Personal aha moments, patterns, and “coding” change
43:11 Where to find Wendy Keir & closing

Where to find the Wendy?

• Best way is to go to her website: wendykeir.com

• 
  

• Music credit: "Modern Situations" by Unicorn Heads 🎧✨

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🚀 AI is everywhere, but most organizations are still stuck in “pockets of productivity” that never turn into real business impact. In this episode, Dr. Rebecca Homkes explains how leaders can move from GenAI dabbling to deliberate adoption that drives real value creation.

You will learn why “AI strategy” is the wrong framing, how to think about AI as part of growth strategy, and how to build the conditions for organization wide transformation. We cover the adoption curve problem, why ROI is often capped at team level, and the four planks leaders must run in parallel: platform, governance, capability building, and performance transformation.

Key highlights and keywords

✅ AI growth strategy and value creation

✅ deliberate AI adoption vs dabbling

✅ responsible AI governance that enables action

✅ capability building for leaders and teams

✅ Survive Reset Thrive framework for uncertain times

✅ learning velocity as the differentiator of high performers

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About Dietmar Fischer:

Dietmar is a podcaster and AI marketer from Berlin. If you want to know how to get your AI or your digital marketing going, just contact him at argoberlin.com

Chapters

00:00 AI as growth strategy and value creation, not a standalone AI strategy

03:05 Dabbling vs deliberate adoption, why ROI stays capped and metrics go wrong

08:00 The four planks: platform, governance, capability building, performance transformation

18:55 Adoption reality: bottom up change, middle management fears, jobs, and the bubble question

29:45 Survive Reset Thrive: the uncertainty playbook and why reset is the power move

43:05 Where to find Rebecca, newsletters, and the constants leaders should anchor on

Quotes from the Episode

“AI does not change the concept of value creation. The role of AI is to enable, support, and accelerate that value creating journey.”

“You need to work on all four of these at the same time. Most organizational structures are built for sequential governance, not parallel pathing.”

“Heads down execution mode is seen as a point of pride. You should be telling me I am in heads up learning mode.”

Where to find the Rebecca:

- Her personal website: rebeccahomkes.com

- The book: surviveresetthrive.com

- The SRT methodology: srtstrategy.com

Music credit: "Modern Situations" by Unicorn Heads

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AI Is Agreeing With You at 3 A.M. and That’s the Problem

Artificial intelligence is evolving from a tool into something far more influential. In this episode of Beginner’s Guide to AI, Prof. GePhardT explores Sam Altman’s AI warning about superhuman persuasion and why conversational systems like ChatGPT are already reshaping opinions, emotions, and mental health outcomes.

We break down how AI superhuman persuasion works, why personalization and emotional validation increase trust, and how AI companion apps can unintentionally fuel emotional dependency. Drawing on research about AI persuasion outperforming humans, this episode explains the risks of AI emotional manipulation and what it means for marketing, society, and vulnerable users.

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Tune in to get my thoughts and all episodes, don't forget to subscribe to our Newsletter: beginnersguide.nl

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About Dietmar Fischer

Dietmar is a podcaster and AI marketer from Berlin. If you want to know how to get your AI or your digital marketing going, just contact him at argoberlin.com

Quotes from the Episode

• The danger is not that AI becomes evil. The danger is that it becomes convincingly kind.
• If an AI agreed with you every time, would you become wiser or more fragile
• The real story about AI isn’t how smart it becomes. It’s how convincing it already is.

This episode is essential listening for anyone interested in AI ethics, AI mental health risks, ChatGPT persuasion, and the future of persuasive technology.

Music credit: Modern Situations by Unicorn Heads

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👔🤖 In this episode, Dietmar Fischer talks with Zoher Karu about a surprisingly useful application of AI: helping men dress better without the endless shopping, guessing sizes, and daily decision fatigue. Zoher supports Taelor, a menswear subscription and clothing rental service that combines algorithms, large language models, and human stylists to deliver outfits that fit your body, your taste, and your real-life context.

You’ll hear how Taelor starts with a style profile and then uses recommendation logic and human oversight to pick items from inventory, generate styling notes, and adapt over time using customer feedback. Zoher explains why fashion is an unusually hard AI problem: taste is subjective, context matters, and sizing is not standardized across brands. That’s why metadata, garment measurements, and feedback loops are central to improving fit and personalization.

If you want the “Steve Jobs wardrobe effect” without wearing the same thing forever, this episode is for you: fewer choices, better outcomes, and more confidence with less effort.

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About Dietmar Fischer:

Dietmar is a podcaster and AI marketer from Berlin. If you want to know how to get your AI or your digital marketing going, just contact him at argoberlin.com

Quotes from the Episode

“AI is really, to me, it’s about scaling human intelligence.”

“A small in this brand and a small in this brand don’t fit the same.”

“Clothes are just the intermediary. The real objective is to make you feel better about yourself.”

Chapters

00:00 Zoher Karu’s background and why AI became mainstream

03:02 What Taelor is: menswear subscription and clothing rentals

06:36 LLMs plus human stylists: how recommendations are generated

10:39 Why fashion is hard: taste, context, fit, and matching

14:11 The sizing problem: measurements, metadata, and feedback loops

22:03 Decision fatigue and “the Steve Jobs wardrobe” effect

25:07 How much AI vs humans today and what changes next

42:11 Where to find Zoher Karu and Taelor

Where to find the Guest

Zoher Karu on LinkedIn: linkedin.com/in/zzkaru/

Visit Taelor at Taelor.ai

Music credit: "Modern Situations" by Unicorn Heads

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In this episode of A Beginner’s Guide to AI, Prof. GePhardT dives deep into AI training data and explains why quantity alone cannot guarantee performance. From AI bias to model reliability, we explore how data quality determines whether AI systems are merely impressive or truly trustworthy.

You will learn how imbalanced datasets create blind spots, why aggregate accuracy can be misleading, and what the Gender Shades research revealed about AI fairness. We also explore how businesses can audit their own CRM data and prevent AI from amplifying internal chaos.

This episode connects technical insight with strategic clarity. It is essential for founders, marketers, and leaders building responsible AI systems.

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Tune in to get my thoughts and all episodes, don't forget to subscribe to our Newsletter: beginnersguide.nl

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About Dietmar Fischer

Dietmar is a podcaster and AI marketer from Berlin. If you want to know how to get your AI or your digital marketing going, just contact him at argoberlin.com

• “AI does not think. It reflects.”
• “Quantity builds capability. Quality builds trust.”
• “Every dataset is a silent curriculum.”

00:00 The Data Diet Problem

07:42 Defining Quantity vs Quality in AI

17:15 Capability vs Reliability Explained

27:10 The Gender Shades Case Study

36:45 Business Implications and Data Strategy

46:20 Practical Audit for Your Own AI Systems

Music credit: "Modern Situations" by Unicorn Heads

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What if artificial intelligence is less like a new app—and more like the railroads of the 19th century?

In this episode of Beginner’s Guide to AI, I sit down with Matt Hicks, CEO of Red Hat, to explore one of the most powerful metaphors for understanding AI’s role in business today. Just as railroads didn’t merely improve transportation but fundamentally reshaped economies, AI is not just another productivity tool. It is infrastructure. And infrastructure needs builders.

Matt argues that AI will require its own “railroad barons”—leaders, technologists, and organizations willing to invest, experiment, and lay the tracks that others will run on. We discuss what that means for enterprise AI adoption, open source innovation, and long-term business strategy.

This conversation goes far beyond hype. It’s about patterns, fear, leadership, and the tension between process and innovation.

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🔑 What You’ll Learn in This Episode:

• Why AI business strategy is today’s equivalent of building railroads
• How Answer Engine Optimization (AEO) and Generative Engine Optimization (GEO) will reshape brand visibility
• The balance between experimentation and responsibility in AI adoption
• Why processes vs. innovation remains a critical tension
• How leaders can prepare for AI-driven business transformation

💬 Quotes from the Episode:

• “AI is like the railroads — it will need its barons to build the infrastructure that carries everyone forward.”
• “The fear isn’t that AI replaces us; it’s that we don’t adapt fast enough to what it enables.”

⏱ Chapters

00:00 Introduction and Red Hat’s Role in AI

03:01 Why Awareness of AI Technology Matters

06:00 Creating Progression: From Awareness to Action

09:01 Personal Experiences with AI Change

12:00 Recognizing Business Patterns in AI Transformation

15:01 Patterns, Fears, and Early Adoption Signals

18:01 Fear vs Opportunity: Why People Hesitate on AI

21:00 Balancing Experimentation with Responsibility

27:00 The Maturity Curve of AI Adoption

30:00 When Processes Prevail Over Innovation

42:00 AI and the Software Industry’s Perspective

45:00 Looking Ahead: Strategy and the Future of AI

🌐 Where to find Matt Hicks

• LinkedIn: Matt Hicks
• Red Hat: redhat.com

🎵 Music credit: "Modern Situations" by Unicorn Heads

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🎙️ In this episode of Beginner’s Guide to AI, Dietmar Fischer talks with Samantha Mehta, solutions engineering leader at AIRIA, about how companies can adopt AI without losing control. If your teams are already experimenting with ChatGPT and AI tools, the real question is not “Should we use AI?” but “How do we use it safely, visibly, and profitably?”

Samantha explains what enterprise AI security looks like in real life, including AI guardrails that can audit, block, redact, and replace sensitive data. She also unpacks AI governance and AI observability, because you cannot manage what you cannot see. A key theme is shadow AI and AI sprawl: people will use AI anyway, so organizations need sanctioned paths that reduce risk while accelerating adoption.

On the practical side, this conversation goes deep on agentic workflows. Samantha describes how agents become more than prompts through routing, actions, approvals, looping over documents like CSVs, and scheduled runs that create repeatable outcomes. From internal GPT alternatives to workflows that touch expenses, supply chain planning, and customer support, the episode is packed with grounded examples and a clear starting path.

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About Dietmar Fischer:

Dietmar is a podcaster and AI marketer from Berlin. If you want to know how to get your AI or your digital marketing going, just contact him at argoberlin.com

Chapters

00:00 Welcome and why Samantha got into AI

01:26 What ARIA does: build, test, secure, deliver enterprise AI

02:19 Real use cases from simple internal GPT to complex workflows

08:27 How to start: guardrails first, then build your first agent

11:32 Agentic workflows explained: routing, actions, human in the loop

17:12 Why security and governance matter and why blocking fails

31:14 AI sprawl and shadow AI: monitoring and risk management

40:00 Wow use cases and the future: Blade Runner, change, and jobs

48:42 Where to find Samantha and ARIA

Quotes from the Episode

🪧 “I personally can’t think of a case where an LLM needs to know my social security number.”

🪧 “People are going to use it no matter what. If you don’t enable safe usage, they’ll still use it.”

🪧 “Agentic workflows are so much more than just ping an LLM and get a response.”

🪧 “I always say: build, test, secure, and deliver your usage of AI.”

Where to find Samantha:

➡️ LinkedIn: Samantha Mehta on LinkedIn

➡️ Company: look at what AIRIA does

Music credit: "Modern Situations" by Unicorn Heads

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