AI and Grey Beards

I began my career in 1972, and we were called Data Processing. What we were doing in 1972 was processing data. Today, we are called Information Technology, or the AI Division, or whatever. And do you know what we are doing today? Processing data. Any questions?Today I want to build on an article by Joe Procopio titled “The End of AI as Human Replacement?” which explores a fascinating recent move by Ford Motor Company. His core story is this: Ford just rehired 350 experienced “gray beard” engineers after discovering that artificial intelligence and automated systems “failed to deliver the desired quality level.” In other words, a flagship BigCo bet heavily on replacing seasoned human talent with AI—and then had to reverse course.

What has your team been able to hand to a business leader from your TOGAF or other enterprise architecture work, that they could read and make a strategic decision from, without an architect in the room to explain it?" That question almost always surfaces the gap EACOE is built to fill.

Why Record Numbers of Enterprise Architects and Business Architects Are Still Failing StakeholdersThe enterprise architecture and business architecture professions face a disturbing paradox. With certifications surpassing 100,000 worldwide and the architecture market valued at $1.44 billion, we have never had more certified practitioners. Certified professionals commanding 25% higher salaries. There are more certified practitioners in the market than ever, and architecture certifications have grown substantially worldwide over the last decade. Yet stakeholder satisfaction with the relevance, usability, and impact of architecture recommendations remains weak, reinforcing a growing concern that credentials are increasing faster than practical competence. The certification industry has created a gold rush, but what are practitioners actually learning?That is not a coincidence. It is the predictable result of a profession that has overinvested in exam performance and underinvested in actual skill development.

The InfoWorld article “Small language models: Rethinking enterprise AI architecture” argues that as large language models (LLMs) hit limits of scale, cost, and risk, enterprises are shifting toward small language models (SLMs) that are faster, cheaper, and more private for well-defined, repetitive tasks. It highlights three primary advantages: division of labor between small and large models, radical economic efficiency for high‑volume workloads, and improved privacy at the edge when models run on devices or on‑premises. The underlying message is that competitive advantage will increasingly come from how well organizations architect and orchestrate multiple models around their own data, workflows, and governance.

Ford’s new electric truck program is being called its “Model T moment” – not because it’s just another vehicle, but because Ford is tearing up a century of manufacturing practice to build something fundamentally different. In doing so, they have replaced the traditional assembly line with what they call an “assembly tree”: a modular way of building that uses far fewer parts, far less complexity, and a completely new production logic.Enterprise Architecture (EA) and Business Architecture (BA) are at a similar crossroads. Most organizations are still using an architectural “assembly line”: long sequences of phases, hand‑offs, documents, and exams that were designed for a slower, more predictable world. These legacy, exam‑centric certifications, methodologies, and frameworks add steps and artifacts, but struggle to produce architectures executives actually use to run and change the business.The Enterprise Architecture Center Of Excellence (EACOE) and The Business Architecture Center Of Excellence (BACOE) represent the modern alternative. They are not “another framework” running down the same line. They are the architecture equivalent of Ford’s assembly tree: a new way of producing outcomes, built around a small number of powerful, reusable components that can be combined quickly to solve real business problems.

Enterprise Architecture and Business Architecture are at the same inflection point that software engineering has reached. When a field confuses vocabulary for competence, titles for capability, and exams for professional readiness, it creates a class of certified people who can describe the work without being able to do the work.That model is no longer good enough. The next era of Enterprise Architect and Business Architect certification must move away from exam-centric credentialing and toward practitioner-based professionalization grounded in evidence, judgment, and demonstrated delivery.

Enterprise Architecture (EA) and Business Architecture (BA) have moved decisively toward the center of enterprise transformation in the last 30 days, especially as organizations try to operationalize AI at scale. Recent market and thought-leadership signals show a clear shift away from architecture as static documentation and toward architecture as a business decision capability focused on speed, risk, investment, and operating-model change.

In a recent CIO article, by Martin De Saulles, “How effective are semantic hubs in moving agentic AI forward?” the authors argue that semantics are now the backbone of enterprise AI, especially as organizations rush to deploy agentic AI systems at scale. They highlight a critical shift: the challenge is no longer just moving and storing data but ensuring that data means the same thing wherever and however it is used.That is precisely the problem space Enterprise Augmented Intelligence (EAI™) was designed to address. And it is the space EACOE and BACOE have been working in for more than fifty years.

This topic is one that should sound familiar to anyone who has been around enterprise architecture, transformation, or banking, as one example, for more than a few years: everything old is new again. The latest version of the old story is being told through “purchased models,” “off-the-shelf ontologies,” and what some are now calling the semantic operating system for banking, as one example. Buying somebody else’s model of your industry is not a new idea. It is a very old idea in very new packaging. Twenty-five years ago, it was sold as reference architectures, industry models, enterprise blueprints, and packaged best practices. Accenture sold it. IBM sold it. The Big Four sold it. Entire consulting practices were built around the claim that if you adopted a prebuilt model of your enterprise, you could move faster, reduce risk, and leapfrog the painful work of figuring out your own organization. But here is the Real Talk.

There is a lot of noise right now about Artificial Intelligence (AI) strategy, and one phrase you have probably heard is this: “Your AI strategy will fail if you do not fix your data debt.”Now, there is truth in that. Years of fragmented systems, inconsistent definitions, and patchwork reports really are catching up with us. AI will expose every weakness you have buried in your data.But if you take that message into the C‑suite or the boardroom, and you lead with “data debt” and “process standardization,” you will lose the audience that matters most.Today, I want to reframe the issue. Your biggest AI problem is not data debt. Your biggest AI problem is executive relevance.The real issue: multiple versions of the truth.