A Beginner's Guide to AI

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

AI ethics is increasingly about more than bias, safety and regulation. It may also be about who controls the knowledge that AI systems use to shape our understanding of the world.

In this episode of Beginner's Guide to AI, Dietmar Fischer talks with Peter Hardi, Professor Emeritus of Economics and Business and a long-time specialist in business ethics, academic integrity and responsible management.

Hardi became seriously interested in AI after seeing how universities were initially responding to ChatGPT. Instead of focusing primarily on detecting students who used AI, he argued that the more important question was how students and professors could use AI in ways that genuinely benefited learning and teaching.

From there, his interest became much broader.
To understand AI properly, Hardi went back to its foundations: mathematics, algorithms, probability, statistics, optimisation and the way these elements come together in modern AI systems. He also became fascinated by the language used to describe AI, arguing that terms such as "learning", "reasoning", "understanding" and "remembering" can make people assume that AI systems possess human-like qualities they do not actually have.

The most important part of the conversation, however, is what happens when AI becomes an intermediary between people and knowledge.
AI systems can distribute information at enormous scale. Hardi asks what happens when those systems begin influencing not only what people know, but also what they consider important enough to learn, preserve and pass on to future generations.
That leads to one of the episode's central questions:

Who decides what goes into the foundational knowledge behind AI?
The discussion covers AI ethics, academic integrity, AI literacy, hallucinations, AI bias, foundation models, AI governance, open models, the EU AI Act, AI in higher education and the impact of AI on fine arts and culture.
It also includes Hardi's very personal perspective on using AI at more than 80 years old.

🎧 Who should listen?
This episode is relevant for business professionals, founders, consultants, marketers, executives, educators, academics and AI decision makers who want to think beyond AI productivity and ask deeper questions about governance, responsibility and knowledge.

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About Dietmar Fischer
Dietmar Fischer is a podcaster and AI marketer from Berlin.
If you want help with AI strategy or digital marketing, visit:
Argo.berlin

💬 Quotes from the Episode
“My concern is really different. What worries me is the concentration of largely uncontested power over decisions about what goes into the foundational training materials.”“These systems can really produce remarkably human-like outputs, but that doesn't mean that they think or understand in the way humans do.”“Curiosity does not have an expiration date.”

⏱️ Chapters
00:00 Opening: AI over 80
04:00 Why universities should teach responsible AI use
14:09 Going back to the foundations of AI
25:27 How AI could reshape cultural knowledge
29:44 Who controls the knowledge behind AI?
38:48 AI, creativity and the fine arts
43:20 Terminator, the Matrix and the future of humanity

🔎 Where to Find Peter Hardi
LinkedIn:
Peter Hardi on LinkedIn
ResearchGate:
Peter Hardi on ResearchGate

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Why AI Agents Aren’t Ready for Business
Why autonomous AI still struggles with reliability, cost, security, and practical business value.
🤖 AI agents have been presented as the next major transformation in business. They can plan tasks, use tools, send messages, access files, and automate entire workflows. But outside Silicon Valley and software development, how many companies are actually getting reliable value from them?

In this episode of Beginner’s Guide to AI, Dietmar Fischer takes a critical look at AI agents for business. Drawing on his own experience as an entrepreneur and AI marketer, he examines why many agent projects take too long to build, need constant supervision, break without warning, and can cost more than the work they were designed to replace.

One agency outreach agent eventually helped produce several new clients, but only after months of configuration. Other attempts were less successful. Automated LinkedIn posts generated little engagement. An AI-generated client document contained errors. Tools such as Zapier and n8n required more setup work than the expected benefit could justify.

💼 The business problem is not only technical. AI agent risks include incorrect customer communication, damaged trust, lost files, deleted emails, data protection concerns, and unpredictable token consumption. When an agent touches several systems, one small failure can affect an entire workflow.

The episode also presents a more practical alternative: small, controlled AI apps. Instead of asking an autonomous system to manage an open-ended process, a company can build a focused tool that performs one defined job. Dietmar discusses vibe-coded apps for formatting invoices and processing meeting notes, built with tools such as Lovable or Replit.

🎯 In this episode, you will learn:

Why AI agents work better for programmers than for many business users
Why most companies underestimate AI agent setup and maintenance costs
How to think about AI agent ROI
Why occasional tasks are often poor candidates for automation
How AI agents can create security and reputation risks
Why human oversight is still necessary
How AI apps differ from autonomous AI agents
Why software-like reliability is essential for employee adoption
What must change before AI agents become normal business tools
The article in Wired: https://www.wired.com/story/why-normal-people-arent-using-ai-agents/

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💬 Quotes from the Episode
“In business, it is much harder to find the cases where AI agents really make sense.”“They cost a lot of time to set up, they break constantly, and they can destroy files, delete emails, or ruin trust.”“You have to have something that works like software and not like a beta.”

⏱️ Chapters
00:00 Do You Actually Use AI Agents?
01:34 Why the Year of AI Agents Hasn’t Arrived
03:07 What Happens When Businesses Build Agents
05:03 The Hidden Costs and Risks of AI Automation
07:50 Why AI Agents Are Not Ready to Close the Loop
08:58 AI Apps as a More Practical Alternative
10:15 Token Costs, Reliability, and Employee Adoption
11:31 Which AI Agent Use Cases Actually Work?

🎙️ About Dietmar Fischer
Dietmar is a podcaster and digital marketer from Berlin. If you want to know how to get your AI or your digital marketing going, just contact him at argoberlin.com.
Hosted on Acast. See acast.com/privacy for more information.

Why Your AI Works Perfectly Until It Doesn't
Edge Cases, Blind Spots and the Failures Nobody Tests For

🤖 Every AI system has a comfortable middle and a neglected edge. In the middle everything works: the typical customer, the standard query, the well-lit product photo. At the edge sits everything else, and that is where artificial intelligence quietly, confidently falls apart. This episode is about edge cases, the rare and ambiguous situations no dataset fully contains, and why they are not a bug to be patched away but a permanent feature of how machines learn.

🐱 We start with a model that called a cat in a knitted jumper a loaf of bread with 94% confidence, then unpack the machinery behind such failures: why rare events are only rare individually while being collectively constant, why confidence scores measure plausibility rather than understanding, why models take shortcuts (the wolf classifier that had actually learned to spot snow), and why data drift makes healthy systems rot without anyone noticing.

🚗 Then the stakes rise. The case study examines the fatal 2018 Tempe crash involving an Uber self-driving vehicle and Elaine Herzberg, using the official NTSB report HAR-19-03. The system detected her six seconds before impact but never settled on what she was, because she was a pedestrian pushing a bicycle. Alongside it we look at Gender Shades by Joy Buolamwini and Timnit Gebru, where highly accurate facial analysis systems showed error rates near 35% for darker-skinned women.

🛠️ We close with practical guidance: how to red team any AI tool in twenty minutes, five questions to ask every vendor, and why "a human is in the loop" is the beginning of a safety plan rather than the whole of one.

✨ Key Highlights
🎯 Edge cases, outliers, corner cases and out-of-distribution inputs
📊 Why AI confidence scores mislead, and what calibration means
🐺 Shortcut learning, from snow-detecting wolves to ruler-detecting diagnostics
🍰 Edge cases explained entirely through cake
⚠️ Four stacked failures behind the Tempe crash
🧠 Automation complacency and why better AI weakens human oversight
🔍 A twenty-minute exercise to break your own AI tools

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🗣️ Quotes from the Episode
💬 "Most AI systems don't fail in the middle. They fail at the edges."
💬 "Elaine Herzberg wasn't an edge case. She was a woman walking her bicycle home."
💬 "If a system fails on you nearly every time, you aren't an edge case in your own life. You're just a person, made into one by whoever decided what counted as normal."
💬 "Anyone selling you a system that has solved edge cases is selling you a system whose edge cases they simply haven't found yet."

👤 About Dietmar Fischer
Dietmar is a podcaster and digital marketer from Berlin. If you want to know how to get your AI or your digital marketing going, just contact him at argoberlin.com
Hosted on Acast. See acast.com/privacy for more information.

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