REAL Talk With Sam Holcman

I want to talk about legacy thinking. Not old thinking. Not wrong thinking. But thinking that was right - sometimes brilliantly right - and then calcified into doctrine. Look up at the sky. Something remarkable just happened in space. Blue Origin flew New Glenn - their massive, orbital-class rocket - for the third time. And they flew it on a booster they already landed and refurbished. The same first stage. Flying again. That is not a space story. That is a legacy story. And yes, there was a glitch on cargo release. The exact same pattern is playing out in enterprise architecture and business architecture right now. The certification became the thing itself. Organizations stopped asking whether the certification and framework served the mission - and started asking whether the mission was being performed according to the certification and framework. Do not keep flying Atlas V missions in a New Glenn world. The new paradigm is not coming. It has arrived. The only question left is whether your organization will be the one catching up - or the one leading the way.

Organizations are learning a costly lesson: AI does not fail first because of the model; it fails because of the data foundation beneath it. More than half of generative AI projects were abandoned after proof of concept by the end of last year, largely because organizations lacked the data readiness required to move from controlled pilots into production environments. This is precisely why EACOE matters. The Enterprise Architecture Center Of Excellence provides the disciplined, practitioner-based framework required to turn scattered, inconsistent, under-governed enterprise data into an architecture that AI can trust, interpret, and scale against. The EACOE AI Data Modeling Master Class operationalizes that discipline by teaching organizations how to build the semantic, governance, and modeling foundation that production-grade AI now demands. This broadcast is based on the work of Joanne Carew titled How poor data foundations can undermine AI success, CIO – April 17, 2026

Let us be honest - most Enterprise Architects and Business Architects start the day the same way. You roll out of bed, scroll through overnight emails, open at least six tabs of frameworks you will only partially read, warm up yesterday’s coffee because the meeting starts in five minutes, and think…Maybe today I will finally fix that capability model. Because let us face it - every good architect knows their day does not really start until they have had that first cup of caffeine. And speaking of coffee - let me share something interesting that caught my attention. Despite what your friend who refuses to drink anything, but single‑origin pour‑over might tell you, becoming a real coffee expert is not easy. And oddly enough, that is a problem for Wall Street. Starbucks may be on every corner, but qualified coffee graders - the folks who certify bean quality for the commodities market - are in serious short supply. According to the Wall Street Journal, these graders endure a brutal three‑stage competency program: a written exam, a three‑hour coffee grading session, and then a live tasting in front of proctors, identifying defects right down to the bean. Miss one - and you start over. Only five to eight percent pass. That is tougher than passing the California bar exam – or any EA or BA exam I have seen. And it got me thinking: imagine if our industry had that kind of rigor. In Enterprise Architecture and Business Architecture, too many certifications promise instant expertise. Take TOGAF®, for example - you memorize a framework, pass a multiple‑choice test, and suddenly you are called “certified.” Or the Business Architecture Guild’s® CBA® certification - memorize definitions, answer questions, check a box. It is like calling yourself a coffee grader because you can tell the difference between a cappuccino and a macchiato. Look, learning theory matters. But real-world skill does not come from picking the right answer on a multiple-choice exam - it comes from building, evaluating, and adapting. That is why I sometimes say: certifications that only test recall create “credentialed beginners.” You may have sixty percent of the vocabulary, but not the muscle memory. And that is exactly where EACOE and BACOE take a different approach.

CIOs are under intense pressure to harness AI to drive efficiency, innovation, and competitive advantage - but as a recent CIO article states on reimagining business processes makes clear, the first step is not to “automate faster,” but to rethink how work actually happens before a single model is deployed. In this context, the EACOE Process Visualization approach emerges as a best‑practice methodology for reworking processes in preparation for AI, ensuring that organizations automate optimized workflows, not legacy inefficiencies.

If your AI and analytics investments still feel like disconnected projects instead of an enterprise capability, this episode of Real Talk with Sam Holcman shows how Palantir quietly turned ontologies into a strategic moat - and how the same ontology‑driven principles behind EACOE and BACOE can turn your architecture into an execution engine for real business outcomes.

If you are a CIO, CTO, or Architecture Manager, you are already paying an “architecture tax” you never approved. It shows up as overlapping platforms, programs that cannot finish, and “strategic” projects that quietly die after burning millions. The surprising culprit: ten everyday EA/BA words that your organization thinks it understands - but does not. When these words are fuzzy, your architecture is fuzzy. And fuzzy architecture is expensive.

In both EACOE enterprise architecture and BACOE business architecture, ontology is the backbone: it tells us what kinds of things exist in the enterprise, how they relate, and how those meanings stay consistent as we automate, integrate, and apply AI. Today, that makes ontology not just a theoretical idea, but one of the most valuable, underused skills in the AI job market – and a critical success factor for serious Enterprise Architecture and Business Architecture work. What an Ontology Really Is: Kinds of Things Together and Their Relationships In information and computer science, an ontology is a formal description of knowledge in a domain – the kinds of things (concepts/classes) and the relationships between them. It is more than a glossary; it is a structured model of meaning that both humans and machines can use.

Welcome to Real Talk with Sam Holcman - where we stop confusing activity with progress and start talking about how businesses actually work. Today I want to talk about something touchy in the Business Architecture world: the difference between belonging to a club and practicing a discipline. Specifically, the world of the Business Architecture Guild® and its BIZBOK® Guide on one side… and BACOE, the Business Architecture Center Of Excellence, on the other.

Many CEOs excel in vision, leadership, and financial acumen - yet remain only vaguely familiar with the disciplines of Enterprise Architecture (EA) and Business Architecture (BA). They hear the words. They see the frameworks. But they do not always connect with the business value that drives strategy, change, and decision-making. That gap can be dangerous.

Enterprise Architecture (EA) is shifting decisively from theory-heavy, exam-driven certification to demonstrated, practice-based competence that delivers measurable business outcomes.

Organizations have been stockpiling data for years, expecting that one day it will become a strategic asset. With generative AI, that moment has arrived - but without disciplined data practices, the promise quickly turns into frustration. The differentiator is no longer access to powerful models; it is the ability to shape, govern, and trust the data that feeds them, which is exactly the gap the AI Data Modeling Masterclass is designed to close.

Let us start with the uncomfortable truth: large language models - ChatGPT, Gemini, Claude, Perplexity, and their peers - are built on a foundation that looks suspiciously like “copyright infringement as a service.” These systems could not exist without consuming massive quantities of human-created text, images, and code. In another industry, that would be called derivative work. In AI, it is called innovation.

Business Architecture (BA) is undergoing the same transformation: away from framework-centric, multiple-choice exams and toward practitioner-based competence proven through real work, with BIZBOK/CBA®-style certification as a well-known example of the older model, not the only one.

There is something absurd happening in enterprise architecture and business architecture today, and it is time we called it out. People are passing multiple-choice exams… or cramming through a sixteen-hundred-page framework manual, and then being told, Now, go modify it. Customize it for your organization. Modify it? Before you have ever used it? Based on what? That is not mastery. That is madness.

Most EA principles sound good but do not change a single decision because they are not testable. To make them real, convert each principle into a SMART goal plus 1–3 hard metrics. Now you can wire that into governance (architecture reviews, portfolio decisions, roadmaps) and report progress quarterly. At that point, it stops being a poster on the wall and becomes a scoreboard the business can hold EA accountable to. Let us explain why Architecture Principles faded away in the 1990's - let us not go backwards in time.

This Broadcast is a review and analysis of the Jan 22, 2026 - by Koenraad Schelfaut and Andrew Long CIO Article titled How to get your enterprise architecture ready for agentic AI. This article misses the central - and fatal - flaw in its reasoning: it assumes that enterprise data is reliable enough to empower agentic AI when, for most organizations, it is not. A concise 90-day, EACOE EA-anchored roadmap you can use with CIOs and sponsors is provided.

“If you are worried about the cost of going for it, just calculate the cost of staying where you are." We have all been there - staring at an opportunity that feels just out of reach. Maybe it is the next career move, a new certification, launching that business idea, or stepping up into leadership. The first thought that comes to mind? “Can I afford this? But here is the truth most people never calculate: staying where you are, has a cost too - it is just hidden.

We are living in an age of endless low‑quality information and recycled self-declared “best practices.” Framework tourists. Certification logos with no delivery record. 1,600‑page manuals that need another 800 pages just to implement, and most recently, another rambling set of explanations on the explanations! Your brain was not designed to drink from this firehose. Critical Ignoring means fighting the instinct to “consume everything” and instead curating a very small set of sources.

In a world of infinite content and finite attention, the real competitive advantage is not posting more – it is choosing better who you listen to. In this episode, Sam Holcman breaks down 39 distinct online personas that dominate LinkedIn, Twitter, and professional communities - from the signal-boosting trusted advisor to the humblebrag hero and the engagement farmer. This is not about shaming individuals; it is about sharpening your discernment in a noisy, performative digital landscape. The challenge is simple: look at who you follow and ask, “Is this person helping me think more clearly, act more wisely, and deliver more value?” Time is precious. Aim it at high-integrity, trusted advisors - not at the loudest voices in the feed.

The scientist who helped invent core building blocks of modern AI just walked away from Meta - with a blunt verdict: Large Language Models (LLMs) are a dead end. In this episode of Real Talk, Sam Holcman unpacks why LLMs are fundamentally flawed as a foundation for serious enterprise decision-making - and walks through a live example of confident, credible, and completely wrong AI output. The takeaway is simple and uncomfortable: the answer is not “more prompts” or “more tokens.” The answer is to stop blindly feeding the generic LLM machine and start building your own Enterprise Augmented Information™ (EAI) - an architected, governed data backbone that combines processing power with actual context and understanding, on your terms, for your enterprise.