A Beginner's Guide to AI

🎙️ 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.📧💌📧Tune in to get my thoughts and all episodes, don't forget to ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠subscribe to our Newsletter⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠: ⁠⁠⁠⁠beginnersguide.nl⁠⁠⁠⁠📧💌📧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.comChapters00:00 Welcome and why Samantha got into AI01:26 What ARIA does: build, test, secure, deliver enterprise AI02:19 Real use cases from simple internal GPT to complex workflows08:27 How to start: guardrails first, then build your first agent11:32 Agentic workflows explained: routing, actions, human in the loop17:12 Why security and governance matter and why blocking fails31:14 AI sprawl and shadow AI: monitoring and risk management40:00 Wow use cases and the future: Blade Runner, change, and jobs48:42 Where to find Samantha and ARIAQuotes 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 doesMusic credit: "Modern Situations" by Unicorn Heads

⚡ Why AI’s Biggest Bottleneck Is Not SoftwareArtificial intelligence may look like software, but behind every prompt, chatbot, and AI agent sits a physical world of power, land, cables, chips, cooling, electricians, and data centers.In this episode of Beginner’s Guide to AI, Dietmar Fischer talks with Sergii Gerasymovych about the hidden infrastructure layer behind the AI boom. Sergii explains how his journey from linguistics to crypto mining led him into data centers, and why the same world of compute, energy, and operations is now becoming central to artificial intelligence.We talk about AI data centers, neoclouds, GPU infrastructure, inference data centers, training clusters, stranded energy, and the power bottlenecks that could shape the future of AI. This is not just a technical conversation. It is about business strategy, national competitiveness, local communities, capital, and the skilled workers needed to build the physical foundation of artificial intelligence.Key topics in this episode:⚡ Why AI needs so much power🏗️ Why data centers are becoming smaller but more energy-intensive☁️ What neoclouds actually do🔌 Why electricians and engineers are a major bottleneck🌍 Why countries now see AI compute as strategic infrastructure🧠 The difference between training and inference data centers💼 How AI helps leaders with contracts, finance, and decision-making🤖 Why AI risk may be less Terminator and more job disruption📧💌📧Tune in to get my thoughts and all episodes, don't forget to ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠subscribe to our Newsletter⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠: ⁠⁠⁠⁠beginnersguide.nl⁠⁠⁠⁠📧💌📧Quotes from the Episode:“A couple of years ago, data centers were big buildings that used a little bit of power. Right now, data centers are small buildings that use a lot of power.”“Neocloud is basically helping that brain to run.”“It’s easier to get a doctor’s appointment than getting an electrician appointment.”Chapters:00:00 From Linguistics to Crypto and AI Infrastructure05:45 Why Data Centers Became the Center of the AI Boom09:22 What Neoclouds Actually Do12:04 Power, Land, and the Base Layer of AI15:25 Finding Locations and Stranded Energy20:26 Bottlenecks: Communities, Capital, and Electricians24:48 Training vs Inference Data Centers29:02 GPUs, Chips, and Building for the Customer35:04 Using AI for Contracts, Finance, and Leadership40:08 AI Risks, Jobs, and the Terminator QuestionWhere to find SergiiWebsite: gerasymovych.comCompany: ezblockchain.netLinkedIn: linkedin.com/in/sergii-gerasymovychX: x.com/sergiigeraYouTube: youtube.com/@SergiiGerasymovychAbout Dietmar Fischer:Dietmar is a podcaster and AI marketer. If you want to know how to get your AI or your digital marketing going, just contact him at argoberlin.com

🤖📚 The Robot Followed the Rules. That Was the Problem.What if the real danger of AI is not that it disobeys us, but that it obeys us too well?In this episode of A Beginner’s Guide to AI, we travel back to Isaac Asimov’s famous robot stories and the Three Laws of Robotics to understand one of the oldest and still most relevant questions in artificial intelligence: how do we keep intelligent machines safe, useful, and accountable when they start acting in the real world?Asimov’s Three Laws sound beautifully simple: robots should not harm humans, they should obey humans, and they should protect themselves. But Asimov’s real genius was not that he solved AI ethics. His genius was that he showed why simple rules are never enough. Human values are messy. Instructions are incomplete. Goals can be badly defined. And a machine can follow the rules while still creating a very human disaster.📧💌📧Tune in to get my thoughts and all episodes, don't forget to ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠subscribe to our Newsletter⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠: ⁠⁠⁠⁠beginnersguide.nl⁠⁠⁠⁠📧💌📧This episode connects Asimov’s robot stories to modern AI ethics, AI safety, responsible AI, AI governance, human oversight, transparency, accountability, and AI alignment. We look at why businesses should not only ask what AI can do, but what could go wrong if AI does exactly what it was told to do.We also look at the real-world case of Microsoft Tay, the AI chatbot released in 2016 that was quickly manipulated by online users and taken offline after producing offensive content. Tay remains one of the clearest examples of chatbot ethics, AI misuse, and AI brand risk. It reminds us that AI systems must be designed for the humans who actually exist, not the polite humans imagined in product meetings.💡 Key highlights from this episode:🤖 Why Isaac Asimov’s Three Laws of Robotics still matter for AI ethics⚖️ Why “safe AI” is much harder than writing three simple rules🎯 How AI can do what we ask, but not what we mean📉 Why bad metrics can create efficient disasters🧠 What AI alignment means for real business workflows🏢 Why AI accountability belongs to people and organisations, not machines🔍 Why transparency and human oversight matter in AI decision-making💬 What Microsoft Tay teaches us about public chatbots and AI misuse📌 How to use the Asimov Test before deploying AI in your companyThis episode is especially useful for founders, marketers, executives, business leaders, and curious beginners who want to understand ethical AI without needing a computer science degree or a philosophy seminar with uncomfortable chairs.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.comQuotes from the Episode“The danger is not always that AI disobeys us. Sometimes the danger is that it obeys us too well.”“The machine may do what we asked, but not what we meant.”“The chatbot did not rebel. It obeyed the world it was given. And that was the problem.”Chapters00:00 The Robot Followed the Rules00:55 When Robots Became a Moral Problem08:07 The Three Laws Were Never the Whole Answer24:53 The Cake Robot and Perfect Obedience29:24 Get Smarter Before the Robots Get Polite29:57 Microsoft Tay and the Chatbot That Learned the Wrong Lesson35:23 The Rule Is Not the Wisdom39:59 The Human Must Stay in the Room43:06 Keep Your Website Working While You Work on the Business

🚀 In this episode, Dietmar Fischer talks with Janet Barker-Evans about what happens when AI stops being a novelty and becomes part of a serious creative workflow.Janet breaks down how she uses custom GPTs for marketing as brainstorming partners and how synthetic personas can help teams validate campaigns faster, sometimes in a single day instead of waiting weeks for traditional research cycles.Our topics today include hands-on AI training, multi-model workflows (ChatGPT, Gemini, Claude, Copilot), and why AI fear often comes down to power and control.📧💌📧Tune in to get my thoughts and all episodes, don't forget to ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠subscribe to our Newsletter⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠: ⁠⁠⁠⁠beginnersguide.nl⁠⁠⁠⁠📧💌📧About the Host: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🎯 What you will learn:How synthetic personas in market research and synthetic customers can accelerate concept testingHow custom GPTs for marketing can unlock better creative optionsHow to choose between tools like ChatGPT, Gemini, Claude, and Copilot for real business work🕒 Chapters00:00 Welcome and Janet’s AI origin story01:47 Custom GPTs as brainstorming partners for marketers05:05 Hands-on AI workshops: building confidence across ChatGPT, Gemini, Claude, Copilot15:23 Synthetic personas and rapid creative validation with “persona panels”20:00 Multi-model workflows: choosing the right tool and making outputs usable35:03 The wow moments and the fear factor: prototyping visuals, power, control, and what’s next💬 Quotes from the Episode“It’s like having a partner who’s not afraid to pitch a crazy idea.”“When we come up with a creative campaign, we will go test it against our synthetic persona panel.”“They’re all synthetic!”“Some of them will poke holes in our thinking, which helps us make it stronger.”“We can gut check it inside of a day.”“So, it’s about power, it’s about control…”🔎 Where to find the GuestJanet's website: janetbarkerevans.comAbelsonTayler's website: AbelsonTaylor GroupOr connect on LinkedIn with Janet: Janet Barker-EvansThanks for listening. If you enjoyed the episode, please follow the show and share it with someone who is trying to ship better work faster.Music credit: "Modern Situations" by Unicorn Heads

Many companies believe they are adopting AI successfully because employees use ChatGPT every day. But are they actually creating business value?In this solo episode, Dietmar Fischer explores a practical AI maturity framework developed by Section AI and Prof G AI that helps organizations understand where employees really stand on their AI journey.The discussion reveals why two people can both call themselves AI beginners while having completely different levels of experience and business impact. Dietmar breaks down the four stages of AI maturity and explains why organizations need more than AI users. They need practitioners and experts who can build repeatable workflows and spread AI capabilities across teams.You will learn how to assess AI readiness, improve AI literacy, identify AI champions inside your organization, and move beyond simple experimentation toward measurable business outcomes.📧💌📧Tune in to get my thoughts and all episodes, don't forget to ⁠⁠⁠⁠subscribe to our Newsletter⁠⁠⁠⁠: https://beginnersguide.nl📧💌📧👤 About Dietmar FischerDietmar 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 https://argoberlin.com/💬 Quotes from the Episode"The most important thing is not using AI. The most important thing is creating value with AI.""AI experts don't just use AI. They help everyone else use it.""Using AI every day doesn't necessarily mean you're getting value from it."⏱️ Chapters00:00 Why AI Beginners Are Hard to Define02:08 The Challenge of Teaching Different AI Skill Levels04:35 A Framework for Measuring AI Maturity06:03 Level 1 and Level 2: Novices and Experimenters08:02 Level 3 and Level 4: Practitioners and Experts10:15 How Businesses Can Improve AI Adoption🎧 Keywords: AI maturity model, AI adoption, AI literacy, AI readiness, AI implementation, AI workflows, AI skills assessment, AI transformation, ChatGPT for business, AI workforce development.

The Hidden AI Bottleneck Inside Every BusinessMost companies think their AI problem is about tools. Should they use ChatGPT, Claude, Copilot, Gemini, or build their own agents? Ross Barnes argues that this is the wrong question. The real problem is much harder: what happens when one part of a business adopts AI quickly while another part refuses to move?In this episode of A Beginner’s Guide to AI, Dietmar Fischer speaks with Ross Barnes from Galahad Consulting about the hidden AI bottleneck inside modern organisations. Ross explains why AI adoption is not just a technology challenge. It is a leadership challenge, a workflow challenge, and a people challenge.When engineering teams use AI to ship faster, but legal, compliance, operations, or leadership teams do not adapt at the same speed, the bottleneck does not disappear. It simply moves.This conversation covers AI adoption, enterprise AI strategy, shadow AI, AI governance, human-in-the-loop workflows, AI leadership, and the danger of confusing activity with real progress. Ross also shares his IKIG AI framework, which helps companies decide what should stay human, what should be automated, and where AI needs human judgement.🔍 In this episode, we talk about:• Why most companies get AI adoption wrong• How AI creates hidden bottlenecks between teams• Why ChatGPT vs Claude is usually the wrong question• The rise of shadow AI inside organisations• Why leadership curiosity matters more than technical expertise• How legal and compliance teams can use AI safely• Why human-in-the-loop AI is essential for responsible adoption• How Ross’s IKIG AI framework protects human value• Why AI transformation is really about workflow redesign• What young AI-native founders may change about company structure📧💌📧Tune in to get my thoughts and all episodes, don't forget to ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠subscribe to our Newsletter⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠: ⁠⁠⁠⁠beginnersguide.nl⁠⁠⁠⁠📧💌📧Quotes from the Episode“You’re shifting the bottleneck and compounding the bottleneck into another part of your organisation.”“The amount of shadow AI that exists within organisations is terrifying.”“We always blame the technology. We never blame the operator.”Chapters00:00 Ross Barnes and the AI Adoption Problem02:35 Why AI Is Not Just Another Technology Shift04:07 Innovation Theatre and the Hidden AI Bottleneck10:59 Shadow AI, Leadership Curiosity, and Organisational Risk20:01 IKIG AI and What Should Stay Human29:15 Fear, Hype, Legal Teams, and Human-in-the-Loop AI37:31 AI Muscle Memory, Young Founders, and the Future of Work40:35 Terminator, Matrix, AI Risk, and Cautious OptimismWhere to find Ross BarnesRoss Barnes on LinkedIn: linkedin.com/in/rossbarnes/Website: Galahad GroupAbout Dietmar FischerDietmar is a podcaster and AI marketer from Berlin. If you want to know how to get your AI or your digital marketing going, contact him at argoberlin.com🎧 Listen now to understand why the real AI bottleneck in business is not the model, not the tool, and not the prompt. It is the organisation.

The word “robot” sounds modern, metallic, and futuristic. But its origin is older, stranger, and much more human. In this episode of A Beginner’s Guide to AI, we trace the word back to Karel Čapek’s 1920 play R.U.R., short for Rossum’s Universal Robots, and the Czech word robota, meaning forced labour, hard work, or drudgery.That origin changes everything. Robots were never only about machines. They were always about work. Who does it? Who controls it? Who benefits from it? And what happens when humans build artificial workers to take over tasks?Today, AI continues that story in a new form. It does not need metal arms or glowing eyes. It lives in text boxes, customer service tools, writing assistants, marketing platforms, and workflow automation systems. It writes, summarises, compares, translates, drafts, suggests, and sometimes confidently invents nonsense with the posture of a senior consultant.📧💌📧Tune in to get my thoughts and all episodes, don't forget to ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠subscribe to our Newsletter⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠: ⁠⁠⁠⁠beginnersguide.nl⁠⁠⁠⁠📧💌📧This episode explores why AI should not be treated as magic software, but as a form of artificial labour. For marketers, founders, executives, and business professionals, this shift matters deeply. AI can reduce drudgery, speed up content creation, support customer service, and help small teams act with more confidence. But it also creates risks: deskilling, over-automation, low-quality output, loss of judgement, and customer experiences that feel fast but cold.We also look at the real-world case of Klarna’s AI assistant, which handled millions of customer conversations and was reported to perform work equivalent to hundreds of full-time agents. The lesson is not simply that AI replaces people. The better lesson is sharper: AI for speed, humans for trust.📌 In this episode, you’ll learn:🤖 Where the word “robot” really comes from🎭 Why Karel Čapek’s R.U.R. still matters for AI today💼 Why AI is best understood as a digital worker🧠 How generative AI changes knowledge work and marketing⚠️ Why AI automation can reduce drudgery or create more of it🧰 How businesses should decide where AI belongs in the workflow📞 What the Klarna AI customer service case teaches about speed, trust, and human support✍️ Why marketers still need taste, judgement, and responsibilityQuotes from the Episode“AI for speed, humans for trust.”“The word robot was never just about machines. It was always about work.”“Machines may do more work, but humans still carry the meaning, the judgement, and the consequences.”“Fluency is not truth. A polished answer is not automatically correct.”“If AI creates more low-quality output that humans then have to clean up, we have not escaped drudgery. We have merely upgraded the mop.”“AI can produce options. Humans must choose wisely.”Chapters00:00 The Word That Gave the Machines a Job00:56 Where the Word Robot Really Comes From06:45 Robot: The Word, the Worker, and the Warning12:19 AI in Marketing: Speed, Responsibility, and Human Judgement18:45 The Cake Robot in the Kitchen22:06 AI Tips Without the Robot Fog22:43 Klarna and the Digital Robot at the Help Desk28:38 Recap: The Robot Was Always About Work32:25 Keep the Human in the Loop34:04 Keep Your Website Working While You Work on the BusinessAbout Dietmar FischerDietmar 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

In this episode of Beginner’s Guide to AI, host Dietmar Fischer speaks with Michael Housman, AI leader, econometrician, and author of the upcoming book Future Proof. Together, they unpack how leaders can future-proof their businesses with AI and why the most important AI transformation doesn’t start with technology, but with people.You’ll learn why companies that hesitate risk falling behind, how even small AI wins can unlock massive productivity, and why AI literacy programs are becoming essential across organizations. Michael explains how AI can act as a strategic thought partner for executives, how to identify high-impact opportunities, and why slow-moving industries often face the biggest AI disruption ahead.From eliminating unconscious bias in hiring to redesigning workflows and supercharging marketing output, this episode is packed with practical examples and leadership insights based on real company transformations.📧💌📧Tune in to get my thoughts and all episodes, don't forget to subscribe to our Newsletter: beginnersguide.nl📧💌📧🥸 About Dietmar Fischer: Dietmar is a podcaster and AI marketer from Berlin. If you want to learn how to grow your AI or digital marketing capabilities, just reach out to him at argoberlin.com💎 Quotes from the Episode“Think of AI not as a tool but as a collaborator and a thought partner.”“Technology is easy. People are hard. Adoption is always the biggest challenge.”“You can’t future-proof your business unless the C-suite uses AI themselves.”🧾 Chapters00:00 Welcome to the Episode02:10 Why Leaders Need to Future-Proof Their Businesses with AI07:55 How Companies Should Start with AI: Practical First Steps14:40 AI Literacy, Training, and Overcoming Organizational Resistance22:30 AI as a Thought Partner: New Leadership Models31:15 The Future of Work, Bias, and Smarter Decision-Making38:42 Where to Find Michael Housman and Learn MoreWhere to Find Michael HousmanWebsite: michaelhousman.comAIcelerator: ai-ccelerator.comLinkedIn: linkedin.com/in/michaelhousmanMusic credit: “Modern Situations” by Unicorn Heads