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