Ep. 35 – Fixing the fundamentals of the web: Sharing 55 years of omnichannel experience w/ Steven Pemberton

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.

Noz Urbina sits down with Ashley Faus, Head of Lifecycle Marketing at Atlassian and contributor to TIME, Forbes, and The Journal of Brand Strategy. Together they unpack the power of content intent in boosting demand, exploring the distinction between learn vs. use intent, funnel phases in intent-focused strategies, and AI's role in content creation. The discussion covers streamlining content roles for team synergy, ensuring message harmony, and aligning marketing with audience needs. “I actually think the funnel is a retrospective measurement tool, not a forward-looking strategy tool. Nobody wakes up in the morning and says, ‘Today I shall be in the awareness phase.’ People don’t behave that way. So, I like to frame up the audience journey in terms of a playground, where people can go up, down, sideways; they can chart their own journey.” – Ashley Faus   By the end of the episode, you’ll learn:   The Importance of Content Intent: Understand the difference between learn intent and use intent, and how this knowledge can dramatically improve demand generation and pipeline generation. Redefining the Funnel: Learn how the traditional top-of-funnel, middle-of-funnel, and bottom-of-funnel model is evolving to accommodate content by intent, and why a mindset shift is necessary for effective marketing. AI in Content Creation: Discover the spectrum of content creation from 100% human to 100% AI, and learn how to match the intent with the appropriate approach for creating meaningful content. Audience Intent Mapping: Gain insights into mapping the customer journey to a “playground” model where consumers can chart their own path, moving beyond the linear funnel model to understand consumer behavior more accurately. Content Planning and Strategy: Find out how breaking down content into actionable steps and clear CTAs can guide users more effectively towards their goals. Effective Marketing through Understanding: Learn the importance of tailoring marketing messages to individual preferences and interests, and how understanding the job or message behind each piece of content can lead to more successful engagement. Collaboration and Strategy in Content Marketing: Discover the critical role of collaboration across teams to create consistent messaging, the importance of a multichannel approach, and how to scale content marketing strategies effectively.

Noz Urbina, alongside our host Larry Swanson, go deeper into the transformative power of artificial intelligence across business, creativity, and more. This discussion will uncover how AI is revolutionizing our methods of problem-solving, content creation, and decision-making. Join us as Noz shares his journey through the evolution of AI, highlighting its potential to enhance human creativity and challenge our understanding of technology's role in society. Tune in to explore the complexities, opportunities, and future directions of AI in our rapidly evolving digital landscape. "AI enables us all to be programmers without being coders. It's about understanding the problem, its context, its constraints, and the intended results, and being able to express that logically and clearly, understanding both your own bias and the AI's bias. That's what lets us win the future." - Noz Urbina

Noz Urbina explores the future of web development and content strategy with Steven Pemberton, a researcher whose contributions have shaped the World Wide Web, particularly in markup languages and web standards. Their conversation untangles the complexities of web technologies, focusing on structured content approaches and innovations in digital experiences. "My fundamental point of view is that if it works, you should do it... My problems with AI is that firstly, there's no I in there yet, that it's machine learning but it's not artificial intelligence. So that you can't trust it." - Steven Pemberton

Noz Urbina delves into omnichannel strategy with Cliff Hazell, a founder, manager, and coach who has spent two decades dismantling barriers to exceptional work. Their discussion explores how to unify engagement and communication strategies across multiple touchpoints, moving beyond parallel approaches to create complementary experiences. They examine how organizations can orchestrate content, design, governance, and systems to align with people's journeys and deliver what audiences truly need. "If you automate stuff that is the wrong idea. You now have the bad things automated." - Cliff Hazell