Enterprise Architecture 4.0 - Redefining EA in the age of AI

If you’re an enterprise architect, you might be feeling a mix of excitement and unease right now. AI is redefining our role at an unprecedented pace—so, what do we do about it? Welcome to my first newsletter! ??

Enterprise Architecture 4.0 is about starting new conversations on how to take the architecture profession to the next level in the context of an AI-led world. It is a community, with the practices, tools and artefacts, to guide enterprise architects and organizations navigate an increasingly uncertain future.

Houston, we have a problem!

Because this time we are facing a different future, with unique challenges. No longer do we only seek to use machines to extend our strength and automate repetitions. We have taken it further by automating our thinking, the very cognitive abilities that makes us uniquely human.

Whereas existing operating models and processes won't extend and scale for Agentic AI, there is much we can reuse to build the right foundations. EA 4.0 honors and draws on the past but realizes we also need something new and different.

This puts us architects in a weird position because it goes against many of the tenets we stand for, e.g. stable and scalable foundations achieved through proven frameworks, repeatable processes, principals, patterns etc. But what do we do in a world that is changing in front of our eyes? And in which existing frameworks and processes only take us so far?

AGI is Coming Sooner Than We Think

Sam Altman recently stated that AGI—Artificial General Intelligence—could be reached within a few years.

Sam Altman Time Magazine Article

But anyone who has taken Chatgpt-o3-mini or Deepseek R1 for a good run knows how incredibly capable these current reasoning these models are out of the box, before any fine-tuning. Imagine 100 AI agents working together, each with human-level reasoning or above. How will that work within an organization, its operating model, its processes, data, platforms, applications, and security? How will we govern it?

It is up to the enterprise architect!

Of course I would say that in self-interest. But taking a step back, it is the only role with deep enough visibility into the business strategy, business model, and operating model, as well as into the application, data and technology architectures.

The EA is also an expert in meta modeling, a critical skill for defining and describing relationships between AI Agents, Multi-Agent Systems, and existing enterprise IT platforms and systems.

As we break new ground we will be stretched! AI Agents will push boundaries in every architecture domain. But how will we ensure governance keeps up? Whereas other architects only need to worry about their specific domain, we need to understand everything. Yet it is from this understanding that we can define and orchestrate the whole picture.

In the age of AI, the EA is no longer optional.

I use AI Agent Capability Maturity Levels as a starting point for analyzing and understanding the impact of AI and how EA might need to change. It is based on the CMMM 5-level capability maturity framework, which is commonly used to assess various organizational maturities.

Levels 1 and 2 are certain. Level 3 is probable. Level 4 becomes somewhat speculative, and level 5 is speculative. After all, we have not yet experienced large autonomous agentic AI systems. Yet there are patterns, such as increasing autonomy, that are visible from level 3. We can expect all architecture domains to shift towards AI autonomy with architects increasingly taking a governance role.

Business Architecture

Business Architecture is a blueprint for aligning an organization’s strategic goals with its operational capabilities. It evolves from a static framework of documented capabilities to an intelligent, self-adaptive system.

Initially, business architecture provides a structured view of business functions, ensuring alignment with strategic goals. As it matures, it integrates with IT and data systems, enabling real-time insights and informed decision-making. AI then enhances this foundation, dynamically optimizing processes and value streams. At its peak, Business Architecture becomes fully autonomous—continuously learning, adapting, and reshaping itself to align with changing market dynamics and business priorities.

Data Architecture

Data Architecture defines how data is collected, stored, integrated, and managed across an organization. It establishes the structures, standards, and governance needed to ensure data is accessible, reliable, and aligned with business objectives. Data Architecture evolves from a static, siloed structure to an autonomous, self-optimizing ecosystem.

It first establishes foundational data models, ensuring consistency and alignment with business needs. As it matures, it integrates across systems, enabling seamless data flow and interoperability. AI-driven insights then enhance data governance, automation, and predictive capabilities, making data more dynamic and context-aware. At its peak, Data Architecture becomes fully autonomous—self-curating, continuously optimizing data assets, and proactively adapting to business and regulatory changes in real time.

Application Architecture

Application Architecture defines the structure, design principles, and interactions of software applications within an organization. It ensures scalability, interoperability, and alignment with business objectives by organizing applications into cohesive, modular systems. Application Architecture evolves from a rigid, monolithic structure to an adaptive, AI-driven ecosystem.

Initially, it establishes foundational application frameworks that support core business functions. As it matures, it shifts toward modular, service-oriented designs that enhance interoperability and scalability. AI then augments orchestration, enabling intelligent workload distribution, automation, and self-healing capabilities. At its peak, Application Architecture becomes fully autonomous—dynamically optimizing itself, proactively adapting to demand, and seamlessly integrating with evolving business and technology landscapes.

Technology Architecture

Technology Architecture defines the foundational infrastructure, platforms, and systems that support an organization's digital capabilities. It ensures scalability, security, and interoperability while enabling seamless integration across applications, data, and business processes. Technology Architecture evolves from a static, infrastructure-centric model to a fully autonomous, self-optimizing environment.

Initially, it establishes foundational technology stacks to support core business operations. As it matures, it moves toward cloud-native, software-defined, and highly scalable architectures that enhance flexibility and resilience. AI then drives automation, predictive maintenance, and intelligent resource allocation, reducing complexity and increasing efficiency. At its peak, Technology Architecture becomes fully autonomous—self-configuring, self-healing, and continuously optimizing to align with evolving business needs and technological advancements.

What's Next?

This is just the beginning. I’m building a community of architects ready to tackle the biggest AI-driven challenges of our time. If you’re interested in joining the conversation, let’s connect! Comment below or reach out directly. ??

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