Introduction
Enterprise Architecture (EA) is widely recognized as a business-critical discipline, yet many organizations learn that too much, too rigid, or too early can be just as damaging as having no architecture at all. The real value of EA emerges when it is balanced, right-sized for the business context, aligned to outcomes, and integrated into decision-making rather than imposed as a heavyweight process. EA can be complex and its track record varies, which understandably makes some organizations cautious. However, long-term evidence consistently shows that a disciplined, adaptive EA capability improves strategy execution and technology ROI. As this recent Forbes article (https://tinyurl.com/mwa58bve) highlights, EA has become even more essential in an era where AI transformation is reshaping operating models and enterprise value.
These insights come from experience, not theory
In my 35+ years in enterprise IT and innovation, I have lived, breathed, and at times even enforced Enterprise Architecture (EA) within a Fortune 40 organization. This includes serving as Chief IT Architect as well as spending the last eight years as VP of Innovation. In that role, I drove enterprise-wide innovation and partnered closely with numerous startups. The success factors for small, emergent products are quite different from those for large, scaled platforms, yet EA plays an important role in both. This diverse background has been invaluable in shaping my perspective on the importance of right-sizing EA.
Business storytelling is more important than architecture content
Clear and consumable business storytelling is one of the most important skills in IT and one of the most underdeveloped. EA strategy, capability design, and execution decisions are major organizational choices that influence most of the enterprise. Presenting these decisions in a compelling and understandable way to the C-suite or board is just as important as the architecture itself.
Many IT professionals believe that more content, more diagrams, and more detail produce better outcomes. In practice, this often erodes trust. Leaders become overwhelmed and begin to question whether the IT organization can focus on what matters most. At the same time, good EA requires thoughtful vetting and debate. Teams need safe spaces for deep technical discussion, disagreement, and exploration of options.
My guiding principle is straightforward. Engage widely and deeply with the team to ensure the technical content is sound. Then invest the time required to condense, clarify, and shape the material into a message that is consumable for senior leaders and business partners. Pay attention to format, readability, the sequence of ideas, and the overall narrative. Remove jargon and distracting terms that pull attention away from the objective. Effective EA requires discipline in both content creation and communication.
Harvest and modify architecture rather than invent and reinvent
A common pitfall for EA teams is the tendency to start from scratch in pursuit of the optimal organizational architecture. While the desire to design the perfect long-term solution is understandable, the reality is that a single “ultimate” architecture rarely exists. A far more effective mindset is to identify teams that already have proven components and use those elements as the foundation for the target architecture. This approach consistently produces higher adoption and success rates.
I think of this as finding a few strong partners within key product areas to build around. Doing so drives greater trust, shared ownership, and stronger alignment with the EA mission and goals. I often say that I would rather harvest and stay nimble than assume I can predict the future well enough to avoid change along the way.
When architecture overreaches, credibility suffers
EA requires clear governance and a defined set of responsibilities. Enterprises must understand whether adoption is occurring and whether outcomes are aligning with the target state. However, I regularly see two patterns that consume energy without delivering value.
The first is the attempt to define and document every aspect of every IT product. This produces enormous workloads and often leads to frustration. Not every detail needs to fall within EA’s scope. A more effective approach is to identify three to five pillars that matter most to enterprise outcomes. These often include data quality and governance, integration and API strategy, and foundational user experience. Even this smaller set of pillars can drive meaningful change, but it keeps the scope manageable.
The second issue is treating all products as equal in governance intensity. Core products that drive major revenue or operational stability require stronger alignment. Emerging or experimental products require lighter touch. One of the biggest mistakes in EA is giving a million-dollar system and a small innovation experiment the same level of scrutiny. This does not mean innovation teams should be ignored. Instead, resources should scale appropriately as products mature or gain strategic importance.
Reducing unnecessary scope and calibrating governance intensity create the focus required for EA to be effective. This also encourages a healthier investment mindset and helps align effort with return.
EA organization operating model is paramount to success
The most important part of EA adoption is often not the quality of the architecture but the credibility of the team. Many organizations struggle because the EA team is viewed as theoretical or disconnected from product delivery. Even strong architectural work struggles to gain traction if the team is not seen as capable partners.
When I helped lead and mature the EA practice, we made a deliberate effort to rotate talent between EA and product-aligned architecture roles. Roughly every three years, we held discussions to move experienced EA leaders into product teams and strong product architects into EA. These rotations required careful selection and were not applied uniformly, but they produced significant benefits.
The goal was twofold. First, leaders who had built real products brought practical insight into EA. They knew what was worth harvesting and what challenges delivery teams faced. Second, EA leaders who moved into product areas gained firsthand knowledge of implementing what they had previously designed. This dual learning model strengthened the organization and raised EA’s credibility across the enterprise.
There is no single recipe for EA success
I have shared practices that have worked consistently throughout my career, but I have also seen very different models succeed in the right context. EA is not one size fits all. Effective EA requires thoughtful adaptation, practical experience, clear communication, and a willingness to evolve as the organization changes.
Let’s continue the conversation. Share your experiences, your challenges, and your perspectives on what works.