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

• 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