One day, people will realize the DATA BELONGS TO ME – remember that quote I sent you from the inventor of the internet and the web. We do not need larger data centers – we need better data – in an organized manner. It begins with a classification system that organizes stuff – data. Think a library – the Dewey decimal system is the key to finding stuff – not endless building of libraries. In one study of medical databases, more than 50% of the total clinical text was found to be duplicated. Google says 60% of the internet is duplicate.

Developing an as-is model before a to-be model seems logical on the surface - understand where you are, then define where you want to go. Yet, in practice, this approach can unintentionally trap organizations with the limitations of their current reality. The deeper issue is not one of modeling techniques, but of human cognition: when people start by describing the present, they often lose their ability to imagine the future.

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.