Ep. 47 – STAIR: A research-backed methodology for adopting AI w/ Louise Harder Fischer and Martin Lassen-Vernal

How do you maintain meaningful work when AI is rewriting your team's job descriptions faster than HR can keep up? This episode introduces STAIR (Social Technical AI Reflection), a structured methodology developed to help organisations navigate the human side of AI adoption, not as a one-time change project, but as an ongoing practice. Noz is joined by Louise Harder Fischer, associate professor at the IT University in Copenhagen, and Martin Lassen-Vernal, head of communications at the City of Copenhagen, to discuss the five core questions that anchor a STAIR session, what a large hospital discovered about nurses and emotional communication, and why continuous reflection is the only honest response to continuous transformation. What you'll learn:  What STAIR (Social Technical AI Reflection) is and why a communications team in Copenhagen created it Why structured reflection is a more honest model for AI adoption than project-based change management The five questions that anchor a STAIR session and how to run one with your own team What a large hospital learned about nurses, patient relationships, and which communications should never be delegated to AI Why job role security — not job security — is the real pressure point for most workers right now How the STAIR methodology differs from top-down governance, and why that distinction matters at scale Where to access the open-source STAIR methodology and starter materials. 🗣️ ABOUT THE HOST: 🔗 CONNECT WITH NOZ: LinkedIn: https://www.linkedin.com/in/nozurbina/ Website: https://urbinaconsulting.com/ Book: https://contentstrategy-thebook.com/   🎙️ ABOUT OMNICHANNELX: The OmnichannelX Podcast explores the intersection of content, customer experience, and technology. Hosted by Noz Urbina, we bring you conversations with industry leaders navigating digital transformation. Website: https://www.omnichannelx.digital/ Newsletter: https://mailchi.mp/645f8e163f9a/newsletter-sign-up-download-book   📧 GET IN TOUCH: Have questions or want to be a guest? Email: [email protected]  

Why does information architecture remain misunderstood despite being fundamental to digital success? In this episode, information architect Dan Brown (Curious Squid) joins Noz Urbina to explore how IA goes far beyond website navigation. Using examples from taco truck franchises to enterprise fleet management, they discuss how structuring information around user questions can improve customer satisfaction, drive efficiency, and support business growth across all channels. "When people ask me for a definition of information architecture, I describe it as the practice of designing structures... creating these frameworks that enable people to interact with information regardless of where or how that information exists." – Dan Brown   🎯 KEY TOPICS DISCUSSED: • Three core business metrics that all decisions ladder up to: growth, customer satisfaction, and efficiency • Frame every use case as a user question - both conscious questions ("How much is my phone bill?") and unconscious ones ("What should I pay attention to?") • Three stakeholder types to address: money-focused (growth/efficiency), experience-focused (satisfaction), and tech-focused (scalability/innovation) • IA creates multiple pathways through the same information based on different user needs and objectives • Move beyond data tables - organize and present data in humane, meaningful ways that tell the right story • Connect user questions to executive concerns - show how efficiently answering user questions impacts the three core metrics • Use narrative storytelling with personas (like "Terry Taco Truck Owner") to make abstract IA concepts concrete and relatable • IA is designing structures that enable people to interact with information regardless of where or how it exists • Database architecture vs. information architecture - database architects store data efficiently, information architects weave it into meaningful stories • The omnichannel imperative - everyone wants access to data in ways meaningful to them across all channels   What you'll learn: Why information architecture is more than just website navigation How to frame use cases as questions users are asking The difference between database architecture and information architecture Why metaphors both help and hinder the understanding of IA concepts How to connect user questions to business metrics The role of IA in omnichannel experiences Practical approaches to selling IA value to stakeholders How different audiences interact with the same information differently   🗣️ ABOUT THE HOST: 🔗 CONNECT WITH NOZ: LinkedIn: https://www.linkedin.com/in/nozurbina/ Website: https://urbinaconsulting.com/ Book: https://contentstrategy-thebook.com/   🎙️ ABOUT OMNICHANNELX: The OmnichannelX Podcast explores the intersection of content, customer experience, and technology. Hosted by Noz Urbina, we bring you conversations with industry leaders navigating digital transformation. Website: https://www.omnichannelx.digital/ Newsletter: https://mailchi.mp/645f8e163f9a/newsletter-sign-up-download-book   📧 GET IN TOUCH: Have questions or want to be a guest? Email: [email protected]  

What if the problem with your marketing isn't that content takes too long to create? Noz Urbina and Bouke Vlierhuis examine how AI has shattered the traditional content workflow, revealing that creation was never the real problem – it was just hiding all the other broken processes. They explore practical AI use cases that actually work, why aerospace engineering principles apply to content strategy, and how to avoid becoming the person who just "looks over the ChatGPT texts."   🎯 KEY TOPICS DISCUSSED: The "Chainsaw Effect" - How AI exposes all your process problems Why 95% of AI projects fail (and how to be in the 5%) The "Fountain Pen Syndrome" - Stop identifying with outdated skills Process analysis is finally "hip" in marketing From 2-week white papers to afternoon content creation Why human-in-the-loop is non-negotiable The coming "trough of disillusionment" and how to prepare   📚 ABOUT THE BOOK: Bouke's "AI Survival Guide for the B2B Marketer" (currently in Dutch) provides practical frameworks for B2B marketers navigating AI transformation. Not another prompt guide, but a strategic roadmap for process transformation 🔗 CONNECT WITH BOUKE: LinkedIn: https://www.linkedin.com/in/boukevlierhuis/ Website: https://www.boukevlierhuis.nl/ Book: https://www.boekenbestellen.nl/boek/ai-survivalgids-voor-de-b2b-marketeer/74852   🔗 CONNECT WITH NOZ: LinkedIn: https://www.linkedin.com/in/nozurbina/ Website: https://urbinaconsulting.com/ Book: https://contentstrategy-thebook.com/   🎙️ ABOUT OMNICHANNELX: The OmnichannelX Podcast explores the intersection of content, customer experience, and technology. Hosted by Noz Urbina, we bring you conversations with industry leaders navigating digital transformation. Website: https://www.omnichannelx.digital/ Newsletter: https://mailchi.mp/645f8e163f9a/newsletter-sign-up-download-book   📧 GET IN TOUCH: Have questions or want to be a guest? Email: [email protected]

What if everything you've been told about AI implementation is backwards? In the latest episode of the OmnichannelX podcast, host Noz Urbina sits down with Lasse Rindom, and they shatter the myth that AI is your secret weapon. Spoiler alert: it's not. With 127 million people using ChatGPT daily, AI has already become as common as Excel. The real question isn't whether you should use AI, but how to stop treating it like a magic wand and start building actual business value. Through 67 episodes of interviewing AI leaders, Lasse has discovered a pattern: companies are failing because they're asking "what can AI do?" instead of "what do we want to achieve?" From exposing why your million-dollar AI investment might be worthless to revealing how "brownfield thinking" can save your transformation, this conversation flips conventional wisdom on its head. You'll discover why context engineering beats prompt engineering, how every business process is secretly about metadata, and why the Wright Brothers' invention of the airplane tells us everything we need to know about where AI is headed. Whether you're a CEO wondering why your AI initiative isn't delivering ROI or a practitioner trying to move beyond chatbot experiments, this episode delivers the tough love and practical wisdom you need to succeed in 2025's AI reality. "If you don't start with outcome and outcome discussion about what you want, then you're not gonna end up with outcome. And that has nothing to do with AI at all." – Lasse Rindom Key Findings AI is a commodity, not a differentiator - With 127 million daily ChatGPT users, the technology itself won't provide a competitive advantage; innovation on top of AI will Process definition precedes successful AI implementation - Software vendors must own and define processes clearly for AI agents to navigate effectively within digital infrastructure Brownfield reality trumps greenfield fantasies - Organisations must work with existing systems and constraints rather than imagining clean-slate implementations Context engineering > Prompt engineering - Pre-prompting and establishing proper context matters more than individual user prompts for reliable AI performance Metadata production is the essence of business processes - Every business process essentially adds metadata to transform inputs into outputs, making AI particularly suited for structuring unstructured data Human change is the limiting factor - Technology adoption speed is constrained by how quickly humans can adapt their mental models and processes Measurement from day one is critical - AI initiatives without clear KPIs and success metrics become expensive experiments rather than business improvement 00:00 Introduction and guest welcome 04:26 AI's role in business and value creation 08:05 "AI doesn't need a push - it's already here" 15:40 The importance of outcome-based AI implementation 18:29 Greenfield vs. brownfield: "The world is brownfield" 23:33 From playground money to real ROI 28:57 Beyond generative: AI as restructuring tool 33:38 "Every process is metadata production" 35:16 Reducing entropy: the true purpose of business 40:34 Context engineering vs prompt engineering 46:56 "The Wright Brothers didn't invent the airline"

In this episode, Noz Urbina interviews Ilya Venger, Data and AI Product Leader at Microsoft, to deliver a masterclass in practical AI implementation for business leaders. Ilya addresses the trillion-dollar question facing every executive: Should we build our own AI solution, buy off-the-shelf, or wait for the technology to mature? His answer: it depends on understanding your specific business problems, not chasing shiny technology. Key Takeaways The 80% Solution: Ilya reveals that AI systems work correctly about 80% of the time. Success isn’t about perfecting that last 20% through expensive fine-tuning – it’s about redesigning processes to work with AI’s probabilistic nature. As Noz puts it, “If you create a workflow with zero tolerance for error, you’ve designed a bad process.” The Fine-Tuning Trap: Ilya shares cautionary tales of companies spending millions to fine-tune models for specific problems (like the “six finger problem” in image generation), only to watch base models solve these issues within 18 months. His stark example: a model fine-tuned to be cheaper than GPT-4 became pointless when GPT-4’s price dropped tenfold. Data Reality Check: Both speakers agree that most organizations have “data heaps” – disconnected silos without understanding or metadata. Ilya’s metaphor: “You’ve got gold nuggets in a dark room. You need to turn on the lights first.” Organisations must understand their data landscape before implementing any AI solution. The Build vs. Buy Decision Framework: Build (Fine-tune): Only when you have extremely specific tasks with proprietary data (like recognizing manufacturing equipment or crop diseases) Buy: For most use cases, using off-the-shelf models with good system prompts and workflow design Wait: When your problem might be solved by next quarter’s model improvements What you’ll learn The build, buy, or wait decision framework – Clear criteria for when to fine-tune models (specific tasks with proprietary data), buy off-the-shelf solutions (most use cases), or wait for base models to improve Master the 80% solution – Why AI works correctly 80% of the time and three strategies to handle failures: improve the AI, modify your processes, or introduce human oversight Avoid the million-dollar fine-tuning trap – Real examples of why custom models become obsolete within 18 months and when fine-tuning makes sense Turn your “data heaps” into AI gold – How to assess and organize disconnected data silos before implementing AI, plus why most organizations fail at this critical first step Design systems, not magic genies – Why thoughtful system prompts and workflow design deliver 10x better ROI than chasing the latest AI model Handle AI’s “alien” failure modes – Understand how probabilistic systems fail differently than traditional software and build processes that expect interpretation errors Find your real competitive edge – Why your IP isn’t in having a custom model but in process design, context setting, and treating AI as “10,000 eager interns” Know when waiting beats racing – Recognise when today’s expensive problem (like the “six finger problem”) will be solved by next year’s base models

Noz Urbina interviews Rafaela Ellensburg, who has pioneered the content engineering discipline at Albert Heijn, one of the Netherlands' largest retailers. Rafaela discusses her journey from content specialist to content engineering leader, emphasising how structured content and metadata enable omnichannel measurement and personalisation at scale. The conversation explores the evolution from content management to concept management, drawing parallels between content supply chains and traditional product supply chains. Key topics include translating strategic business goals into measurable content metrics, implementing knowledge graphs and ontologies for cross-domain data connections, and preparing high-quality structured data to enhance AI reliability. "You allow yourself as an organization to bring forward that message to whichever person it resonates with in the market, and you're able to do it on whichever channel that person is present. You get the relevance, and you get it at scale, at an omnichannel scale—making sure that the right message is sent to the right customer at the right moment and the right channel. That is the marketer's dream, right? That's what we all want." – Rafaela Ellensburg   "I like to compare content to products. People know products—they know shopping, they know logistics, they know that products are created somewhere and then have to be refined before they get to the stores. It's something that people can grasp, but we can do the same thing for content." – Rafaela Ellensburg   "We as humans actually have work to do to make our data of AI quality—more complete, richer, more consistent and truthful, so that whatever the AI does with that data, it becomes better. You do not get garbage in, garbage out, but you get value in, value out." – Rafaela Ellensburg

In this podcast, Noz and Amir Faizpour talk about how businesses can effectively implement AI beyond basic tools like ChatGPT. Amir, who runs Aggregate Intellect, explains why companies might need more sophisticated AI solutions that integrate with their existing business systems rather than using standalone AI tools. The conversation focuses on “AI agents” – systems that can independently use tools and execute complex tasks – and the importance of separating language models from actual business data to ensure accuracy and reliability. A key takeaway is that instead of relying on one large AI model for everything, businesses might benefit more from using multiple specialised models for different tasks, much like how human workflows operate.   What you’ll learn Understanding why businesses might need solutions beyond standard AI tools like ChatGPT and Microsoft Copilot The importance of integrating AI with existing business systems rather than using siloed solutions How “agentic” AI systems work and their three key components: tool usage, memory, and planning capabilities The value of separating language models from actual business data and knowledge Why smaller, specialized AI models can sometimes be more effective than larger, general-purpose ones The role of human oversight and knowledge in AI systems How to evaluate and verify AI-generated content for accuracy and specificity.   “I have these nine commandments that I usually talk about when it comes to generative AI and one of them, which I think is the most important one, is the separating the knowledge, the data and the linguistic interface.” “One of my biggest design principles is that the architecture of, or the anatomy, as you’re saying of the system, you’re building has to replicate the human workflow.” – Amir Feizpour

Noz Urbina joins Larry Swanson to explore a critical question: as AI becomes universal, how will we distinguish real from synthetic? Their discussion delves into Truth Collapse - the AI-accelerated metacrisis that could make or break the world. They examine the challenges facing our institutions, cultures, and individual stability when both simple and sophisticated AIs become an integral part of daily life, and what this means for our collective future.   What you'll learn: The importance of humility and understanding our vulnerabilities to predict and prepare for the future. The psychological manipulation techniques used by tech companies, such as Skinner boxes and addictive design. The role of AI in cultural participation and the associated risks. The potential dangers of AI in terms of emotional manipulation and truth collapse. Legislative measures that could mitigate the negative impacts of AI.

Noz Urbina and Larry Swanson examine the fundamental aspects of AI ethics as artificial intelligence becomes increasingly embedded in society. Their discussion covers five key insights into AI ethics that help safeguard our future, exploring big tech manipulation tactics, corporate power dynamics throughout history, and the role of government regulation in technology. The conversation addresses how we can better understand and navigate these ethical complexities as AI integration deepens. What you'll learn: Understand the dual aspect of AI and technology: potential benefits and significant ethical dilemmas How technologies, particularly AI, are designed to exploit human psychology for profit (e.g., social media platforms using addiction mechanics) Historical roots of corporate power and its implications for modern-day technology practices Insight into the necessity for governmental intervention and effective policy-making to regulate tech companies and protect societal interests An exploration of personal and collective responsibility in fostering a healthier relationship with technology and governance.    

Noz Urbina and Robyn Eastlake discussed the current state of omnichannel in the financial services sector, emphasizing its crucial role in meeting users' needs and staying connected with businesses. They highlighted the importance of content efforts being connected and omnichannel, and offered tips on how to implement successful omnichannel strategies. They discussed the challenges of creating a successful content strategy in the financial services industry, including balancing short-term goals with long-term thinking and leveraging AI in content design and automated experience design. They also explored the potential of AI in content production while maintaining the value of human creativity, and emphasized the need for a hybrid approach that leverages the strengths of both AI and human creativity. "The challenge is always how do you remind everybody in the day to day that [long-term relationship building] is the goal. You have your setup where you have your manufacturing side of the business deciding lending policy and then you have your distribution, still pretty much set up by channel. They're not usually pretty well connected... Making sure that users feel valued, they feel safe, they're protected, and that the things that are being served to them are appropriate to them is hugely valuable for businesses." – Robyn Eastlake Listen now   What you'll learn Omnichannel Mastery: Insights on navigating omnichannel strategy in financial services. Content Meets AI: How AI is shaping content strategies and what pitfalls to avoid. User-Centric Design: The importance of aligning content with user needs and expectations. Navigating Challenges: Key strategies for overcoming common obstacles in financial services. Future Trends: Predictions on the evolution of content and digital experiences.