Claim OwnershipREAL Talk With Sam Holcman
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R.E.A.L. - Realistic, Enabling, Actionable, Logical. Every day we hear jargon and see writing from so-called “experts,” and we don’t know what we should follow and what we should avoid. Published practices aren’t always best practices!
Listen to episodes from Sam Holcman’s radio show, webinars, and podcasts, Real Talk with Sam Holcman. Each episode gets to the bottom of what business executives, managers, practitioners, and staff actually need to create innovative solutions that deliver- no utopia required.
This business podcast provides practice-based insights into business transformation, enterprise architecture, business architecture, organizational transformation, and technology transformation based on real-world practices. We provide you with insights that can provide true value to organizations and individuals that face today’s and tomorrow’s competitive pressures and provide a usable takeaway from each program.
Listen to episodes from Sam Holcman’s radio show, webinars, and podcasts, Real Talk with Sam Holcman. Each episode gets to the bottom of what business executives, managers, practitioners, and staff actually need to create innovative solutions that deliver- no utopia required.
This business podcast provides practice-based insights into business transformation, enterprise architecture, business architecture, organizational transformation, and technology transformation based on real-world practices. We provide you with insights that can provide true value to organizations and individuals that face today’s and tomorrow’s competitive pressures and provide a usable takeaway from each program.
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A thought leader is an authority figure recognized for their expertise in a specific field, often leading discussions and sharing insights that influence their industry. They are known for their ability to innovate and drive change, and they have a significant impact on their community. Self-declaration does not make someone a thought leader. Who are you actually getting information from?
Data drives Artificial Intelligence - not the other way around. It is the foundation upon which AI systems are built and the fuel that determines how powerful, accurate, and innovative they can become. The phrase “Data for AI” captures this reality: without high-quality, strategic data, AI cannot function effectively. Developing a data architecture that prioritizes data at the core unlocks agility, insight, and competitive advantage in the era of intelligent automation.
AI, driven by hype cycles, is disrupting and in many ways undermining the value of REAL (Realistic, Enabling, Actionable, Logical) Enterprise Architecture - turning a professional discipline into yet another fleeting trend much like previous waves of tool-centric or automation-driven approaches. Instead of enabling architectural vision, the rush to “AI everything” is exposing existing weaknesses and, for many organizations, acting as a costly distraction rather than a true solution.
AI is everywhere - on every page, in every boardroom pitch, and across every trend report. But as any seasoned leader and practitioners know, what creates lasting competitive edge is not the technology buzzword of the moment. It is having an operating model that empowers smart choices, no matter what tool is in play.
In this broadcast, I want to challenge the status quo: It is not “AI-powered strategy” that drives transformation. It is strategy-powered architecture - built through proven Enterprise Architecture (EACOE) and Business Architecture (BACOE).
The term “prompt engineering” has become popular in recent years to describe the process of carefully designing inputs - known as prompts - for large language models (LLMs). The phrase suggests that prompting is a deliberate, technical process similar to real engineering disciplines such as civil engineering, electrical engineering, or software engineering. Yet, when examined more closely, “engineering” may not be the most accurate word.
Recent advancements in Artificial Intelligence (AI) have been touted to result in dramatic increases in productivity and consistency throughout Enterprise Architecture practices. However, our experience with our clients demonstrates that excessive dependence on AI-driven support - particularly for critical decisions - can bring significant risks to business operations and strategic integrity.
Enterprise Architectures and Business Architectures often suffer from a common problem: the outputs produced by architecture teams may be technically accurate, possibly methodologically sound, and rigorously documented - yet they remain unusable for the decision-makers, stakeholders, and business leaders who most depend on them. This issue, known as Human Consume-ability, highlights the gap between creating models and architectures, and delivering insights that can be readily understood, trusted, and acted upon across organizational boundaries.
AI - Artificial Intelligence. OI - Original Intelligence. While AI can excel at processing vast amounts of data and identifying patterns, Original Intelligence allows humans to recognize when solutions need to deviate from established norms or when data is incomplete or misleading. Learn more in this Broadcast
There is always a lot that we can learn from successful technology and software developments. There is also a lot we can learn, unfortunately, from technology and software development “failures”. In analyzing over twenty well-documented and publicized failures, one fundamental issue came through loud and clear. A major mismatch between the enterprise data representations and processes, and the vendor’s data representations and processes. There is a pretty straightforward way to address this situation.
Yes, anything you say to AI can be used against you, including things that you thought were deleted. Yes, hitting the delete button or key really does not do anything. Let me translate. Public facing AI and associated models are “dangerous”. EAI – (Enterprise Augmented Information) – AI within the boundary of your Enterprise is the answer. Please listen. And we can help.
What is the oil that makes the artificial intelligence world run? Data. Data infrastructure is the key to moving away from guessing to true usefulness. Data infrastructure is made up of two parts – the actual technology, and the data itself. Billions are being spent on the technological side. The data itself is being starved. Why? Because it is hard. We will describe the five transformations needed to make data AI ready.
This broadcast is a commentary. One line I am quoting – “AI models should treat content taken from social media and the internet as untrustworthy sources.” Large Language Models (LLM) ARE the issue, and there is no solution to this – what is public is subject to manipulation. What is the answer to this issue? EAI – Enterprise Augmented (Almigated) Information. Also known as Small Language Model.
Enterprise architecture (EA) and Business Architecture (BA) certifications are often seen as strategic moves - intended to elevate professional credibility, further organizational maturity, and equip leaders with frameworks and methodologies to drive business optimization and transformation. But, unfortunately, most certifications do not live up to these expectations.
The hype around Artificial Intelligence (AI) continues to accelerate. Billions are being spent in industry, by investors, and by you and I. Beyond FOMO (Feal Of Missing Out), where is all of this going?
This Broadcast provides some valuable insight and questions you need to ask, as you take this rollercoaster ride. Fasten your seat belt and be sure you also have a tight harness.
The adoption of Artificial Intelligence (AI) to develop Enterprise Architectures (EA) is attempting to transform the field, but it comes at a cost: a noticeable decline in traditional EA competency and growing skepticism among stakeholders as to the worth of Enterprise Architecture. Contemporary Enterprise Architects expertise is no longer just a nice-to-have - it is a strategic imperative for any organization seeking to thrive in an AI-driven world.
This is a post from Peter Kinev that I will be embellishing a bit. Somehow, over time, Enterprise Architecture has developed a language of its own. A language that seems more designed to deflect than to inform. I think it is time we change the narrative.
This historical synergy between engineering and manufacturing int the industrial age offers a powerful analogy for understanding the complementary relationship between EACOE Architecture Models and EACOE Implementation Models for Enterprise Architecture, in the EACOE methodology for Enterprise Architecture (EA). Just as the collaboration between engineers and manufacturers powered the Industrial Revolution, the partnership between EACOE architecture models and EACOE implementation models is crucial for successful enterprise transformation. One without the other is incomplete: architecture without implementation remains theoretical, while implementation without architecture risks inefficiency and misalignment.
This somewhat extended broadcast of Real Talk brings forward the fundamental truth facing success use of AI – most organizations data is not at all ready for use in AI (or for that matter, decision making outside of AI). You will hear a bit of sarcasm in my voice, and at the end, yes a commercial on how to actually build a quality data environment. Wishing it were not true, will not make it so.
In the quest to optimize budgets, many organizations focus heavily on minimizing upfront costs - especially when it comes to Enterprise Architecture training, and certification decisions. However, prioritizing initial cost savings above all else can be a risky strategy, often leading to long-term inefficiencies, increased risks, and diminished personal, professional and organizational credibility.
How do you get someone to click on your article, product version, advertisement, or post? Just put AI or artificial intelligence in the headlines. Click bait has been around since the advent of the internet. But you need to read past the headline to understand what actually it takes to “use” AI – Data, as this broadcast describes with an actual example.
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