Samuel Holcman: Thank you for tuning in to Real Talk. Be sure to join your host Sam Holcman again for another edition of our program. We'll have more Real Topics of discussion then. How do you sort out the so-called jargon from real-world practices that work? Do the members of your organization find some business or technology advice utterly confusing? Welcome to Real Talk with Sam Holcman. In this program, we set the record straight and in terms that business people and technology people can understand. Now, here is your host, Sam Holcman. Welcome to Real Talk with Sam Holcman. I'm Sam Holcman. Today I want to talk to you about a hard truth that a lot of organizations are quietly living with in their AI. That is, projects look great in a demonstration and then quietly die when it's time to go into production. You've probably seen the statistics by now. By the end of last year, more than half of generative AI projects were abandoned after the proof of concept. in large part because the organization simply was not ready from a data standpoint. The model was not the real problem. The real problem was the foundation under it, the data. In other words, it is not that AI doesn't work. It's that the AI cannot compensate for poor quality, poorly structured, poorly governed data. If your data is unreliable, incomplete, and misunderstood, your AI will be unreliable, incomplete, and misunderstood. What I want to do in this episode is to connect that problem to something that you practically could use and actually do about it. Because this is where EACoE Enterprise Architecture, data modeling, and specifically EACoE's AI data modeling masterclass become not just helpful, but actually absolutely essential. Let's start with what really happens inside most AI proof of concept projects. When a team builds a proof of concept, they usually put their very best foot forward. They pull together curated data sets. They do manual workarounds. They build in a tightly controlled environment. Everyone in the room sees the demonstration and says, wow, this is amazing. Let us scale it. But as soon as you move out of that controlled environment, reality hits. The rest of the enterprise does not look at anything like that curated sandbox that you are using. Data scattered across dozens or hundreds of systems. Definitions are inconsistent. Metadata is missing a wrong. The same concept of customer means different things in different systems. And nobody can quite agree on which version of the truth is actually the truth. So the proof of concept works in the lab and then collapses under the weight of real world data. On top of that, most of your existing governance was designed for humans reading reports, not for AI agents consuming and acting on data at machine speeds. The policies, the controls, the permissions, many of them assume a human in the loop interpreting and applying judgment. AI changes that. Now you have non-human consumers operating at different speed and scale, and your governance model simply does not stretch that far. This is why so many AI initiatives hit a wall. Not because the algorithm is weak, but because the underlying architectural disciplines around data were never built for this AI world. So where does EACoE come in? EACoE, the Enterprise Center of Excellence, exists to do something extremely and explicitly specific, to give organizations a practitioner-based, repeatable method for building the architectural foundations that AI actually needs. This is not about putting more slides in a strategy deck. This is about building concrete artifacts, ontologies, data models, governance structures, and implementation roadmaps that move you from data chaos to AI ready. One of the core ideas in EACoE is that you start with the business, not the technology. You begin with a set of high value use cases, five to 10, not 500, and you ask, what decisions are we trying to support? What actions do we want AI to take or augment? What information does that require? And how must that information be defined, structured, governed, and delivered so that both humans and machines can trust it? From those questions, you derive the data architecture, the entities, the relationships, the semantics, the ownership, the metadata, the policies. You do not try to fix all data everywhere. You can't. You deliberately strengthen the data foundations around those use cases that matter most and then expand outwards. A very practical approach. This is a classic EACoE discipline applied to AI. Now, let us make that even more concrete by talking about the EACoE AI Data Modeling Masterclass. The Masterclass is designed for the world we are actually living in. A world where large language models already come with a huge amount of general knowledge but cannot understand your enterprise unless you teach them your language, your ontology, your DNA, and give them well-modeled, well-governed data to work with. In this master class, we go way beyond traditional table and column modeling. We teach you how to model your business concepts and relationships as an ontology. so that customers, policy, asset, order, claim, or whatever matters in your world has a precise shared meaning. Define data ownership and stewardship so there is no ambiguity on who is accountable for the quality interpretation of critical data elements. Design the metadata that AI systems actually need, definitions, provenance, classifications, sensitivity levels, security tags, and usage constraints. align your data models with AI era governance, things like least privilege access for agents, auditability for what the AI saw and did, and clear boundaries around what models can, not, and can touch. This is the connective tissue. that poor data foundations really point to. Without this work, AI remains a clever demonstration. With it, AI becomes something you can actually deploy, govern, and scale. By the time you walk out of the AI Data Modeling Masterclass, you're not leaving with vague inspiration. You are leaving with a structured way to identify and prioritize your first set of high-value AI use cases, a first-cut enterprise ontology. focused on those use cases. A set of draft data models and metadata patterns that make your data machine actionable and business trustable. A concrete proposal for updating your data governance to AI aware, which you can take back to your CIO, CDO or executive team. In short, you will know how to turn our AI pilots keep getting stuck into we now have a roadmap, a vocabulary. and an architectural method to get us from pilot to production. If you're listening to this and thinking, that is exactly where we are. Our AI demonstrations look great, but we cannot get them into the real world, then this class, this workshop is for you. EACoE and the Data Modeling Masterclass are built to close that specific gap, the gap between exciting AI ideas and solid data foundations. If you want AI, which your organization can trust, audit and scale, you need more than a model. You need architecture. You need a data ontology. You need disciplined data modeling that fit for the AI era. That's what we do. If you'd like to learn more, get details on the next AI data modeling class, we'd like to bring the AI data modeling class on site. or talk to us about an AI data readiness assessment for your organization, please do not hesitate to reach out to us at It will be our pleasure to provide you with a no-fee session discussing your specific requirements. Until then, I'm Sam Holcman. Thanks for listening.