A Beginner's Guide to AI

👔🤖 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 Beginer’s Guide to AI, Dietmar Fischer speaks with Shaheen Samavati, co-founder and CEO of VeraContent, about what an effective AI content marketing strategy actually looks like inside a real agency.
AI in marketing is no longer experimental. It’s operational.
Shaheen shares how her team moved from testing ChatGPT and OpenAI tools to building structured, repeatable AI workflows for marketing agencies. From briefing and drafting to localization, editing, and publishing, AI now supports both creative execution and backend operations.
This conversation goes beyond surface-level tool talk. It explores what it really means to integrate generative AI in marketing without sacrificing quality, brand voice, or client trust.

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🌍 Leading an international content agency in Spain, Shaheen offers a practical, no-fluff perspective on the “adopt-or-die” reality facing content marketers today.
How AI reshapes content marketing strategy and agency workflows
Why adopting AI is no longer optional in content creation
Balancing brand voice, speed, and quality with generative AI
How clients react to AI-driven content — and what wins them over
Future trends: AI SEO, AI video, AI email tools

Key Themes Discussed
AI Content Creation vs. AI Content Operations: It’s not just about writing faster. AI is reshaping how agencies organize projects, manage briefs, handle multilingual content, and scale output.
Brand Voice & Quality Control in the Age of Generative AI: Speed without editorial structure leads to mediocrity. The real competitive advantage lies in combining AI acceleration with strong human oversight.
AI SEO Strategies 2025: As search engines integrate AI into results pages, marketers must rethink optimization. AI-assisted workflows are becoming essential to stay visible.
Future of AI in Marketing: From AI video generation to AI email tools and automation stacks, the marketing landscape is shifting toward integrated AI ecosystems.

💡 Shaheen's Quotes:
“It’s kind of an adopt-or-die situation for anyone in the content business.”
“We’re moving from testing tools to building repeatable, scalable AI workflows.”

🧾 Chapters (experimental feature)
00:00 Welcome & Episode setup
02:15 Shaheen’s journey & founding Vera Content
07:40 Early experiments with AI in content
12:05 The “adopt-or-die” moment for content marketing
15:30 How AI reshaped content creation workflows
20:45 Backend operations & scaling with AI
25:10 Client adoption & resistance
30:05 Balancing quality, brand voice & speed
35:20 Looking ahead — future of AI in marketing

Where to find VeraContent: 🔗 VeraContent
Where to find Shaheen: 👩🏼‍🦰 Shaheen Samavati
Here is her landing page prompt tutorial on YouTube
And this is the replay of the webinar about AI for marketing teams

🎵 Music credit: "Modern Situations" by Unicorn Heads
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AI systems are often praised for their size. Bigger datasets. Bigger models. Bigger compute. But what if scale is only half the story?

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

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

Chapters
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
Hosted on Acast. See acast.com/privacy for more information.