A Beginner's Guide to AI

Artificial intelligence often feels mysterious. Machines detect spam, recommend products, analyse customers, and power countless digital tools. But behind all of these systems lies a surprisingly simple question: how do machines actually learn? In this episode of A Beginner’s Guide to AI, Prof GePharT breaks down one of the most important concepts in machine learning: the difference between supervised learning and unsupervised learning. You will discover how AI models learn from labelled data when the answers are already known, and how algorithms can explore raw data to uncover hidden patterns without guidance. These two learning strategies power many of the systems shaping modern technology. Using practical examples such as spam filters, customer segmentation, and simple analogies like cake classification, the episode explains how machines learn from data and why the training method makes a huge difference. Key takeaways include how supervised learning works with labelled datasets, how unsupervised learning reveals patterns in complex information, why training data quality matters, and how businesses use both methods to build intelligent systems. 📧💌📧 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.com Quotes from the Episode Supervised learning teaches machines the answers. Unsupervised learning helps machines discover the questions. Artificial intelligence is not magic. It is pattern recognition powered by data. Machines do not wake up intelligent. They become intelligent through training. Chapters 00:00 The Two Ways Machines Learn 06:10 What Supervised Learning Really Means 18:45 Discovering Patterns with Unsupervised Learning 32:20 The Cake Example Explained 40:30 Real World AI Case Study Spam Filters and Customer Segmentation 52:15 Why AI Training Methods Matter Music credit: Modern Situations by Unicorn Heads Hosted on Acast.

Artificial Intelligence is moving from experimentation to everyday business reality. But most organisations still struggle with one key question: How do you actually implement AI across a company? In this episode of Beginner’s Guide to AI, Dietmar Fischer speaks with Jim Spagnardo, enterprise AI strategist at ProArch, about what it really takes to roll out AI inside organisations. Jim explains why AI adoption is less about technology and more about culture, leadership, and data readiness. He introduces the idea of the three Ds of work — the dull, the draining, and the distracting tasks that AI can remove so people can focus on higher-value work. They also discuss when companies should use tools like Microsoft Copilot, when it makes sense to build a custom data and AI platform, and why data governance becomes critical once AI is introduced. If you are a business leader trying to understand how AI will reshape your organisation, this conversation offers a practical look at the challenges — and opportunities — ahead. 📧💌📧 Tune in to get my thoughts and all episodes, don't forget to ⁠⁠⁠⁠subscribe to our Newsletter⁠⁠⁠⁠: beginnersguide.nl 📧💌📧 About the host, Dietmar Fischer: Dietmar Fischer is a podcaster and AI marketer from Berlin. If you want to get your AI or digital marketing projects started, contact him at argoberlin.com. Interesting details and takeaways • Why leaders must mandate AI adoption and how to structure a Smart Start engagement. • The three Ds (dull, draining, distracting) as a simple way to position benefits for end users. • How Copilot reduces context switching and the security/data protections needed to use it responsibly. • Practical, measurable first use cases and how to track success via clear KPIs. • Advice for students and early-career professionals: be a self-starter and learn AI skills now. Quotes from the episode “We have to show people we’re taking away the dull, the draining, and the distracting so they can do creative work.” “There’s nowhere to hide: bad data surfaces weaknesses far faster when you use AI.” “If you’re going to succeed, go after high-value, low-effort, high-return use cases first.” “This affects everybody — it’s not just moving infrastructure; it changes conversations and who you have to talk to.” “Copilot lives inside your environment — users don’t have to context-switch and it knows your organisation.” “Don’t wait for formal education to teach this; be a self-starter and learn before you need it.” Chapters 00:00 Welcome and why Jim got into AI 03:40 From IT conversations to the C-suite: changing who you must talk to 07:05 The three Ds: removing dull, draining, and distracting work 10:40 When to choose Copilot versus building your own data platform 14:30 Copilot advantages and data governance considerations 18:20 Visual reasoning, demos and the “Barcelona photo” moment 22:15 Smart Start: executive briefings, champions and use case workshops 27:00 Writing with AI and transparency in authoring content 30:10 Risks, regulations and advice for the next generation 33:45 Where to find Jim and closing thoughts Where to find the Jim: LinkedIn: linkedin.com/in/spignardo/ Website: ProArch.com Music credit: "Modern Situations" by Unicorn Heads 🎵 Hosted on Acast.

🎙️ Ritish Chugh (Airbnb analytics engineering) joins Dietmar Fischer to unpack a problem almost every company has, but few name clearly: your metrics do not mean the same thing across teams. Finance, marketing, and sales can all talk about “revenue” and still end up in dashboard chaos. The result is wasted time, slow decisions, and leadership that does not fully trust analytics or AI. In this episode, Ritish introduces the idea of the human data pipeline: the person who stitches together conflicting definitions, tribal knowledge, and unspoken assumptions just to answer basic business questions. Then we move into the fix: unified metric definitions, a data dictionary for business metrics, and a semantic layer that acts as a translator between raw data schemas and business meaning. That foundation is what makes natural language querying and conversational analytics viable at scale, without turning AI into a confident hallucination machine. We also cover why AI adoption in analytics stalls when organizations prioritize models and infrastructure but neglect data quality, validation frameworks, and metrics governance. If you want AI to support decision-making, you need governed metrics, clear ownership, and a system that produces consistent answers across BI tools, SQL, and AI agents. Finally, Ritish shares wow moments from using AI tools to summarize years of code and PRs, generate deeper test coverage, and reduce time spent on manual SQL by building agents on top of a semantic layer. 📧💌📧 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.com Chapters 00:00 From data consulting to Airbnb and AI as a junior analyst 02:22 The human data pipeline and why metrics never match across departments 07:32 The fix: unified metric definitions, data dictionary, and the semantic layer translator 13:32 Why AI adoption stalls: data quality, trust, validation, and metrics governance 26:36 Data abundance, experimentation, and AI assisted A/B testing with humans in the loop 33:37 Wow moments with AI, role transformation, and why the Terminator is not invited (yet) Quotes from the Episode “AI just acts like a junior analyst, which is always available for you.” “The first thing is… build that level of data definition that is unified for all.” “No matter what AI models they’re using… if the data… is not up to the mark, it’s not going to give you the right results. It’s always going to hallucinate.” “Every department has a different interpretation and definition of the metric.” “I spend a lot of time really doing reconciliation between the numbers and data…” “The most important thing happening is transformation…” Where to find Ritish: ➡️ You connect with him on LinkedIn: linkedin.com/in/ritish-chugh/ 📌 Keywords you’ll hear in action: semantic layer, data dictionary, metrics governance framework, unified metric definitions, governed metrics, natural language querying, conversational analytics, agentic analytics, data quality for AI adoption. Music credit: "Modern Situations" by Unicorn Heads Hosted on Acast.

🚀 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 📧💌📧 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.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.

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. 📧💌📧 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.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 Hosted on Acast.

👔🤖 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. 📧💌📧 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.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.

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. 📧💌📧 Tune in to get my thoughts and all episodes, don't forget to ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠subscribe to our Newsletter⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠: ⁠⁠⁠⁠⁠⁠⁠⁠⁠ beginnersguide.nl ⁠⁠⁠⁠⁠⁠⁠⁠⁠ 📧💌📧 🌍 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 Hosted on Acast.

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. 📧💌📧 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.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 Hosted on Acast.

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. 📧💌📧 Tune in to get my thoughts and all episodes, don't forget to ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠subscribe to our Newsletter⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠: ⁠⁠⁠⁠⁠ beginnersguide.nl ⁠⁠⁠⁠⁠ 📧💌📧 🔑 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 Hosted on Acast.

🎙️ 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.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.

AI is transforming the real estate industry — but what does that really mean for agents on the ground? In this episode of Beginner’s Guide to AI, host Dietmar Fischer sits down with Andrew Reville, founder of PeakAgent, to explore how artificial intelligence is reshaping the way agents work, market, and connect with clients. 📧💌📧 Tune in to get my thoughts and all episodes, don't forget to ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠subscribe to our Newsletter⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠: ⁠ beginnersguide.nl ⁠ 📧💌📧 From the challenges agents face with lead generation to the opportunities of AI-powered tools, Andrew shares his journey from realtor to tech founder and reveals why the future of real estate belongs to those who embrace AI, not fear it. 🔑 Key Highlights Andrew Reville’s journey from agent to AI entrepreneur The real pain points of real estate agents — and how AI can fix them AI tools for real estate agents 2025 and why they matter How generative AI will transform real estate valuation and marketing The future of property listings, client relationships, and agent workflows 💬 Quotes from the Episode “We didn’t want to just build another AI tool — we wanted to solve real pain points for real estate agents.” “The dream of being an agent often fades when the reality of chasing leads and endless follow-ups hits.” “AI in real estate isn’t about replacing agents — it’s about giving them back the time and energy to love their job again.” “I’ve spoken with dozens of agents, and the question I always ask is: what would make you fall back in love with being an agent?” “Generative AI has the potential to completely change how we value, market, and sell properties.” “The future of real estate belongs to agents who embrace AI, not fear it.” ⏱️ Chapters (experimental feature) 00:00 Welcome & Introduction of Andrew Reville 05:30 Andrew’s Journey: From Real Estate Agent to AI Entrepreneur 12:15 Discovering the Potential of AI in Real Estate 19:40 Building PeakAgent: Solving Pain Points for Agents 27:50 The Harsh Realities of Being a Real Estate Agent 36:20 How AI Can Help Agents Fall Back in Love with Their Work 44:45 Generative AI and the Future of Property Valuation 52:10 AI Marketing Strategies for Real Estate in 2025 59:00 Final Thoughts and Andrew’s Advice for Agents 🌐 Where to find Andrew Reville 🔗 Website: PeakAgentAI.com 🔗 LinkedIn: Andrew Reville 📸 IG: @peakagentai 🧑‍🦰 Personal IG: @andrew_reville 🚀 Paper&Purpose - help Andrew doing good deeds: www.paperandpurpose.me ✨ Tune in to get my thoughts, and don’t forget to subscribe to our Newsletter! 🎶 Music credit: "Modern Situations" by Unicorn Heads Hosted on Acast.

Boobesh Ramaurai on the Future of Data and AI In this episode, I sit down with Boobesh Ramaurai of LatentView to explore the future of data and AI—from his early days in analytics to today’s transformative AI landscape. Boobesh shares how curiosity led him into the world of analytics back in 2006, why execution is more important than ideas, and how data-driven decision making is reshaping businesses across industries. 📧📧📧 Tune in to get my thoughts and all episodes, don't forget to ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠subscribe to our Newsletter⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠: beginnersguide.nl 📧📧📧 We dive into the real-world impact of AI, the challenges organizations face when adopting data strategies, and what it means to build human-centered AI with responsibility and ethics in mind. If you want expert insights into AI in business, responsible AI implementation, and the future of data and AI, this conversation is a must-listen. ➡️ Key Highlights Boobesh Ramaurai’s journey from analytics to AI leadership How businesses can harness data-driven decision making with AI Why execution beats ideas in the world of innovation The growing importance of human-centered AI and responsibility What’s next for the future of data and AI 🧾 Quotes from the Episode “I always say that it is not the idea that really is valuable. It is the execution—that’s the magic and the secret sauce.” — Boobesh Ramaurai “It was fascinating to see how people were using data and capturing data to answer business questions—that curiosity is what pulled me into AI.” — Boobesh Ramaurai 🔗 Where to find Boobesh Ramadurai LinkedIn: linkedin.com/in/boobesh/ LatentView's Website: latentview.com Tune in to get my thoughts, and don’t forget to subscribe to our Newsletter: 💌 beginnersguide.nl Music credit: "Modern Situations" by Unicorn Heads Hosted on Acast.

🚀 In this episode of Beginner’s Guide to AI, Dietmar Fischer speaks with Naga Santhosh Reddy Vootukuri (aka Sunny), a Principal Software Engineering Manager at Microsoft working on Azure SQL deployment infrastructure. Sunny shares his personal journey into AI, from early ChatGPT experiments in late 2022 to using AI tools in production workflows, and what actually changed his day to day work. 💡 You’ll hear how he thinks about GitHub Copilot inside Visual Studio, where it saves time, and where engineers still need to slow down and verify outputs. The episode also goes beyond coding into leadership and adoption: how managers can help teams use AI responsibly, and why showing outcomes and numbers matters more than hype. Sunny also connects the dots to the broader industry shift toward AI agents and structured tooling like GitHub Models and Docker’s evolving AI ecosystem. ✅ Key takeaways you can use immediately Practical AI adoption for engineers and managers GitHub Copilot productivity in real workflows, not demos Why AI code can look correct and still be wrong, and how to respond The rise of AI agents and what it means for everyday teams How GitHub Models lowers friction for evaluating models and prompts Why Docker is leaning into agent workflows and developer productivity 📧💌📧 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.com 🎬 Chapters 00:00 Welcome and Sunny’s background at Microsoft and Azure SQL deployment 00:53 What pulled him into AI from ChatGPT experiments to real workflows 07:50 AI tools and jobs, building websites faster and empowering non devs 10:56 GitHub Copilot in Visual Studio, how it changes daily coding 19:40 The AI adoption gap, why many still do not use AI and the rise of agents 38:45 Docker Captain, GitHub Models, and building agent workflows without heavy setup 42:22 Trust, privacy, and the future facing questions to close the episode 💬 Quotes from the Episode “I recently wrote an article also on Business Insider… how I can save, like, 60% to 70% of my time doing… repetitive tasks.” “Lead by example and lead with numbers… show the actual data… this is how it really improved my productivity.” “Earlier, AI also doing a lot of hallucination… it was generating all crappy code… you have to go and iterate multiple times.” 🔎 Where to find the Guest Docker profile: docker.com/contributors/naga-santhosh-reddy-vootukuri/ GitHub: github.com/sunnynagavo Speaker profile: sessionize.com/naga-santhosh-reddy-vootukuri/ Redgate community ambassador profile: red-gate.com/hub/community/ambassadors/ambassador/Naga-Vootukuri/ And of course LinkedIn 😉: linkedin.com/in/naga-santhosh-reddy-vootukuri-5a67a133/ Music credit: "Modern Situations" by Unicorn Heads Hosted on Acast.

AI Leadership for the Agent Era: Building Hybrid Organizations with Dominic von Proeck AI is entering its operational phase. In this episode, Dominic von Proeck, Co-Founder of Leaders of AI, breaks down what AI transformation looks like when you stop collecting prompts and start building agent-powered teams. We talk about why owner-led companies and the German Mittelstand can move faster than many expect, and why the most important capability is not technical wizardry but leadership: clear delegation, strong feedback loops, and critical thinking about every AI output. Dominic shares how their organization runs AI assistants with real operational discipline, including onboarding, documentation, and even personality profiles, plus the emerging pattern of AI managers that lead other agents. If you want practical guidance on AI agents in business, hybrid organizations, and adoption that sticks, this conversation delivers an unusually concrete operating model. 📧💌📧 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.com Chapters 00:00 Dominic’s AI origin story and why AI transformation matters now 03:10 Mittelstand impact, demographics, and why owner-led firms can move fast 06:10 Adoption reality: AI at home vs at work and the companion effect 08:10 Leadership as the key skill for managing AI assistants and hybrid teams 14:10 The stack and the operating model: agent files, Airtable layer, self-hosting and n8n 17:05 Fear, pain points, and the real path to organization-wide AI adoption 24:00 2026 and the shift from prompts to agents, plus AI managers leading other agents 35:25 Matrix education, flow learning, and what ethical progress looks like 40:45 Where to find Dominic and Leaders of AI Quotes from the Episode “Prompting is 2025… in 2026, we should let the AI prompt.” “One of the best antidotes to being afraid of anything is education.” “To be honest, leadership skills.” Where to find the Guest Website: leadersofai.com LinkedIn: linkedin.com/in/dominicvonproeck/ Programs: The MBAI program Music credit: "Modern Situations" by Unicorn Heads Hosted on Acast.

✨ Unlock a Future Where AI Inspires Leadership—not Replaces It In this episode, Dietmar Fischer speaks with Ja-Naé Duane and Steven Fisher, co-authors of the book SuperShifts, about what leadership really looks like in the age of artificial intelligence. Instead of framing AI as just another technology trend, the conversation explores AI leadership as a systemic and human challenge. Drawing on their work with global organizations and executives during and after the pandemic, Ja-Naé and Steven explain why the biggest shifts are not driven by tools, but by how leaders rethink decision-making, responsibility, and organizational design. The episode traces the origins of SuperShifts back to Covid, when existing systems suddenly stopped working. Ja-Naé Duane shares insights from working with CEOs across Europe who were already using machine learning, but struggled to use AI to meaningfully support leadership decisions. Together, the guests unpack why AI-first leadership requires more than efficiency gains. It demands clear governance, ethical accountability, and a shared understanding of who owns outcomes when humans and machines collaborate. A central theme of the conversation is human-AI collaboration and why leaders must move beyond optimizing outdated structures. Steven Fisher introduces a systems-thinking lens, arguing that organizations need new frameworks rather than incremental improvements. The discussion highlights how AI changes leadership roles, why trust and transparency matter more than ever, and how possibility itself becomes a strategic asset in the age of intelligence. Key takeaways include practical insights into AI leadership, the importance of systems thinking, and why SuperShifts offers a roadmap for leading through uncertainty. This episode is for anyone who wants to understand how leadership must evolve as AI becomes embedded in decision-making, work, and organizational culture. 📧💌📧 Tune in to get my thoughts and all episodes, don't forget to ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ subscribe to our Newsletter ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠! 📧💌📧 ➡️ Key Highlights Understanding AI-First Leadership through the lens of SuperShifts The pandemic's role in inspiring new leadership frameworks and agile mindsets Blending human values with AI-powered decision-making Why systems thinking, foresight, and possibility are essential tools for modern leaders 🧾 Quotes from the Episode - “The most successful leader won’t be the one who predicts the future—but the one who shapes it.” - “In the Age of Intelligence, possibility itself becomes the most valuable capital.” - “Our role as leaders is to bring humanity into the algorithm, not replace it.” 👓 Chapters (experimental) 00:00 Introduction – What is SuperShifts? 05:12 From Pandemic to Paradigm Shift: How SuperShifts Was Born 12:45 AI-First Leadership: Reimagining How We Lead 20:30 Human-AI Collaboration: Balancing Ethics and Innovation 28:10 Systems Thinking and SuperShifts Framework 35:00 Applied Strategies: Leading in the Age of Intelligence 🔗 Where to Find Ja-Naé Duane and Steven Fisher Dr. Ja-Naé Duane: Ja-Nae.IO Steven Fisher — StevenFisher.IO And here you'll find: SuperShifts: Transforming How We Live, Learn, and Work in the Age of Intelligence Music credit: "Modern Situations" by Unicorn Heads Hosted on Acast.

AI adoption is not only a technology shift, it is a leadership and culture shift. In this episode, Dietmar Fischer talks with Bala Muthiah about AI leadership, the psychology behind AI resistance in the workplace, and the practical steps leaders can take to turn curiosity into day to day usage. Bala shares why the human aspect still decides outcomes, even when the tools feel magical. You will learn how leaders can reduce fear, build confidence, and guide teams through real AI upskilling strategy instead of one off trainings that never translate into workflows. The conversation also touches on industry differences, including why sensitive domains like healthcare raise the bar for responsible AI adoption, and what the rise of agentic workflows means for the future. 📧💌📧 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.com 🎧 Chapters 00:00 Welcome and why AI is a leadership moment 02:12 AI leadership in 2026: pressure, performance, and opportunity 04:41 The real barrier: fear, skepticism, and AI resistance at work 07:45 Industry realities: healthcare, sensitivity, and responsible adoption 17:50 A practical framework: upskilling people and building confidence 34:49 The next wave: agentic workflows and what leaders should prepare for 41:43 Where to find Bala and closing thoughts 💬 Quotes from the Episode - “And to me, it’s still human, meaning us, we are still humans, leaders are still humans. The human aspect still stays.” - “Again, I’m coming back to the people, like, because that’s gonna be the unlock for you. Upskill your people with AI tools.” - “AI being, like, the car, or being the internet, being the electricity.” 🌍 Where to find Bala Muthiah: - On his website: balamuthiah.com - His Speaker profile: sessionize.com/bala-muthiah/ - LinkedIn: linkedin.com/in/balaarjunan/ Music credit: "Modern Situations" by Unicorn Heads Hosted on Acast.

🤖🧠 Thinking with Machines with Vasant Dhar What happens when AI stops being a tool and starts becoming a collaborator and an agent? In this episode, NYU Stern professor and AI pioneer Vasant Dhar takes us through the real story behind modern AI, and the practical frameworks we need for AI trust, AI governance, and the coming era of agentic AI. 🚀 What you will learn - Why “thinking with machines” is a bigger idea than “thinking machines” - How the automation frontier separates low-risk automation from high-stakes human control - Why healthcare has lots of data but still struggles to make good decisions - Why mental health is a dangerous place to outsource empathy to machines - What edge cases in AI mean and why they matter for self-driving cars - How AI agents change the governance conversation, from obligations to restrictions to rights 📌 Key highlights - A practical definition of trust in AI based on error rates and consequences - AI in healthcare data: turning medical trails into usable decision intelligence - The future of work: AI as an amplifier, not a substitute, unless you let it become a crutch - Governance questions that no one gets to avoid once agents can act in the world 📧💌📧 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.com Quotes from the Episode 💬 “Trust depends on how often a machine makes mistakes and the consequences of those mistakes.” “In physical health, I’m very optimistic. In mental health, not so.” “It’ll likely lead to a bifurcation of humanity… skills get amplified… or people rely on the machine as a crutch.” Chapters ⏱️ 00:00 Vasant Dhar’s origin story in AI and early expert systems 05:08 A Brave New World warning and why optimism still needs guardrails 07:26 AI in healthcare vs mental health and why feelings change the rules 12:37 The trust heat map and the automation frontier in real life 18:21 Edge cases, bounded rationality, and what machines pay attention to 26:03 The future of work and why AI amplifies both skill and decline 36:23 Governance, AI agents, and how much agency we should allow 44:05 AI wow moments and the next frontier: integrated machine senses 47:15 Where to find the book, podcast, and newsletter Where to find Vasant Dhar 🔎 - Visit Vasant's Website, also to find all the links to shops with "Thinking with Machines", his book: vasantdhar.com - Listen to his Podcast: bravenewpodcast.com - and get his Newsletter: vasantdhar.substack.com Music credit: "Modern Situations" by Unicorn Heads` Hosted on Acast.

Most people think better AI results come from better prompts. This episode proves why that’s wrong. Professor GePhardT introduces Context Engineering, the missing skill that transforms AI from a confused parrot into a capable collaborator. Through relatable metaphors, real business examples, and a deliciously British cake analogy, you’ll learn how shaping an AI’s environment matters more than clever wording. You’ll discover: Why prompt engineering alone fails How context helps AI understand intent The difference between guessing and knowing A real telecom case where context fixed customer support How to apply context engineering in everyday AI use 📧💌📧 Tune in to get my thoughts and all episodes, don’t forget to ⁠⁠⁠⁠subscribe to our Newsletter⁠⁠⁠⁠: ⁠⁠⁠⁠ beginnersguide.nl ⁠⁠⁠⁠ 📧💌📧 Quotes from the Episode “Prompt engineering is asking nicely. Context engineering is setting the stage.” “Without context, AI is guessing. With context, it understands intent.” “Context turns AI from a parrot into a collaborator.” Chapters 00:00 Why Prompts Alone Are Not Enough 04:12 What Context Engineering Really Means 10:25 Understanding Intent Through Context 18:40 Context Engineering vs Prompt Engineering 25:10 Telco Case Study 35:20 The Cake Example 44:00 Final Takeaways 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 Music credit: “Modern Situations” by Unicorn Heads 🎵 Hosted on Acast.

🤖🧠 AI is making strategy cheap. Adoption is still expensive. In this episode, Dietmar Fischer sits down with Bud Caddell (NOBL) to unpack what leaders miss when they roll out generative AI and expect instant results. Bud shares how his team thinks about AI change management, why “turning on Copilot” is not an adoption plan, and what happens to consulting when LLMs can produce “firm-grade” recommendations in seconds. You will also hear the story behind ConsultingSlop.com, a strategy generator that models the reasoning styles of major consulting firms and outputs polished advice instantly. What started as a parody quickly became a serious signal about commoditization, incentives, and the real differentiator: execution, trust, and organizational design. Key takeaways you can apply immediately: ✅ How to approach Microsoft Copilot adoption strategy like a redesign effort, not a software toggle ✅ Why AI literacy and training reduce fear, resistance, and “adoption theater” ✅ What the agents wave means in practice, including platforms like Agentforce ✅ How “vibe coding” changes prototyping speed and risk for teams 📧💌📧 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.com Quotes from the Episode “AI is this incredible wave that I think is gonna fundamentally change individual organizations, but the entire economy, society at large.” “We turned on Copilot, so why aren’t we more productive? … it’s a design process.” “My big prediction is that over the next 18 months, we’re gonna see a lot of backpedaling… and sunk cost fallacy.” Chapters 00:00 Bud’s path from software to organizational change and why AI feels different 04:20 ConsultingSlop.com, vibe coding, and when AI strategy gets uncomfortably believable 06:30 Copilot mandates vs real adoption, why productivity math fails without redesign 16:40 AI as a catalyst for deeper issues: brand story, conflict, and culture 19:25 The next 18 months: investment traps, backpedaling, and what leaders should do 38:00 Agents, Agentforce, and Bud’s personal AI toolkit plus wow moments and wrap Where to find the Guest Bud Caddell: https://budcaddell.com/ NOBL: https://nobl.io/ Consulting Slop: https://consultingslop.com/ LinkedIn: linkedin.com/in/budcaddell/ Music credit: "Modern Situations" by Unicorn Heads Hosted on Acast.

🚀 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 testing How custom GPTs for marketing can unlock better creative options How to choose between tools like ChatGPT, Gemini, Claude, and Copilot for real business work 🕒 Chapters 00:00 Welcome and Janet’s AI origin story 01:47 Custom GPTs as brainstorming partners for marketers 05:05 Hands-on AI workshops: building confidence across ChatGPT, Gemini, Claude, Copilot 15:23 Synthetic personas and rapid creative validation with “persona panels” 20:00 Multi-model workflows: choosing the right tool and making outputs usable 35:03 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 Guest Janet's website: janetbarkerevans.com AbelsonTayler's website: AbelsonTaylor Group Or connect on LinkedIn with Janet: Janet Barker-Evans Thanks 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 Hosted on Acast.