Trends in Enterprise Architecture

Companies are increasingly striving to become more customer-focused, they are changing based on customers’ expectations. Daily, we see new applications and technologies making our lives easier. But for a company that needs to make money (or cut costs), this cannot come at any cost. New technology is only useful if the organization can use it to improve its offering, increase efficiency, and/or reduce costs. Achieving these benefits is not an easy task when vital data, information, and capabilities are wrapped up in complex legacy or scattered systems. Moreover, there are also new consumer demands and new legal and compliance requirements to be met. Most organizations have taken steps to fulfill the promise of digital transformation, whether through methodologies such as ‘agile’ and ‘lean’, technologies such as the ‘Internet of Things’ and AI, or microservices and cloud migrations. However, companies often fail to align and orchestrate this fluctuating architectural landscape, which inevitably leads to the transformation initiative coming to a standstill or being only partially realized. Let’s have a look at the trends in Enterprise Architecture.

Our predictions for 2023 and beyond

It is against this background that companies must find a holistic solution. The speed of change is no longer constant – it is increasing! The ‘modern’ Enterprise Architecture has emerged with the primary responsibility of making the organization (as a whole of processes, technology, and people) sufficiently flexible to cope with an increasing rate of change. In the field of Enterprise Architecture, real flexibility begins with architectural simplicity: learning how companies can reduce (organically grown) complexity and stimulate organizational flexibility.

Faster changes mean that companies can no longer stick to stable long-term plans. Instead, they must constantly adapt to new threats, obligations, and opportunities. And to do so, they must be agile. Agility is usually seen as a measure of how quickly, cheaply, safely, accurately, and repeatably companies can adapt. Less well-known is that agility occurs at two different levels, just like the economy, and that to be successful, companies must excel in both micro-agility and macro-agility.


Micro-agility refers to the business and technology change activities of individuals, teams, and product or project units, and can be improved through the use of techniques such as SAFe, LeSS, iterative software development, DevOps, service automation, the use of cloud-based utility computing, or Robotic Process Automation (RPA). In short, it encompasses all things typically mentioned in discussions about agility and flexibility.

The problem with this is that micro-agility can only be effective when the teams or individuals have enough autonomy and independence to do their work without constantly referring to other teams for information, permission, approval, or for delivery of part of the work. This is where macro-agility comes into play, and it is just as important, if not more so, but it is often not taken into account or considered too late.


Macro-agility applies to business and technology change activities on larger scales such as platforms, programs, business units, divisions, and the entire enterprise. It is partially enabled by an organization’s culture – the degree of autonomy it delegates to business units and its attitude towards innovation – but primarily by its operating model and technology architecture, both of which are designed at the macro level by business and enterprise architects.

In short, macro-agility creates the environment in which an organization’s micro-agility can thrive and implement changes quickly, cheaply, and reliably. Without macro-agility, companies will still struggle to implement changes quickly, no matter how much they invest in DevOps and agile delivery methods. But with macro-agility, significant structural changes, such as mergers and divestitures, and major innovations, such as launching new services or product categories (even legal and judicial changes within Justice), can be routine rather than requiring Herculean efforts to achieve.

Barriers to Flexibility at the Macro Level

What impedes flexibility at the enterprise macro level? In a word: complexity, or what many organizations refer to as technical or architectural debt – is everything that makes systems or processes difficult to understand and therefore difficult to change. Due to the underlying dependencies and interconnections, each new system interface, each database shared between applications, each additional hand-off in a business process, each new governance forum, etc. adds friction and complexity to the whole.

The task of the ‘new’ enterprise architect will be to understand the causes of complexity and to deliver simplicity, as the simpler the system, the faster it can change. Real business value is created when we divide the enterprise in a way that maximizes business flexibility.

Agility in Practice

Modern EA is not about putting up roadblocks and checkpoints that slow down agile teams. It provides guidance and guardrails to steer teams in the right direction and help them implement changes more efficiently, while still leaving room for autonomy and innovation.

Democratizing EA

The democratization of Enterprise Architecture is necessary to arrive at a composable business model, agile enterprises, and the transition to a value-stream-based organization.

Democratizing EA is a form of crowdsourcing, where everyone within the organization contributes to building and documenting the architecture, with the “modern enterprise architect” taking on increasing responsibility for coordinating and facilitating multiple semi-autonomous teams. In terms of documentation, EA can to some extent be considered as the data quality governance responsible for EA documentation.

People outside the central EA team are gaining more and more access to EA documentation, learning to interpret it and actively contributing to maintaining the AS-IS documentation. The business architecture itself is changing from being formally defined and centrally managed to be mass-consumable and self-service. As a result, User Experience (UX) and agility in relation to documentation and underlying tooling have become just as important as frameworks and formal notations.

Of course, democratizing EA requires setting up a mature and performing EA practice within the organization, which also sets up and rolls out the basic documentation and required EA tooling. This is always done in collaboration with a strong internal change management team.

Creating an “Organization’s Digital Twin”

Gaining insights into existing complexities and dependencies, and actively managing and anticipating continuous change requires, in addition to conventional EA information, other types and forms of information. The Organization’s Digital Twin (modern EA documentation) therefore combines conventional EA models with situational and contextual information to improve decision-making; think of organizational information (operating models), financial information (license costs, construction, maintenance, evolutionary trends, depreciation, etc.), socio-technological (which technology is used where, in which processes, in what way and by whom), time-related information (end-to-end delivery process lead times, performance measurements of operational processes), KPIs, event data, trends, etc. This often requires real-time integrations between EA tooling and other business and management applications.

Building such a “digital twin” is necessary to get a more thorough representation of how the company operates, what the impact of possible business ideas is, and also allows different scenarios/futures of the organization to be compared. This gives decision-makers immediate access to more complete, up-to-date, and higher-quality information. It also enables them to better track costs, benefits, and time for projects and delivery. Combining this information streamlines the time-to-market and identifies aspects of the company that need to be re-prioritized.

The same information provides Enterprise Architects with a basis for comparing the proposed benefits of change initiatives with actual business benefits after the change has been implemented (instead of classic business cases used only at initial decision-making).

It can even be used for modeling, where architects can create simulated sandboxes to run different outcomes and scenarios. In any case, the feedback loop is continuous (and increasingly real-time) and provides an accurate overview of how a company operates. The speed at which accurate information is available to semi-autonomous teams and decision-makers contributes structurally to the speed of decision-making, the lead time, and the quality of implementations, and thus to the agility of the enterprise.

Building and integrating the Organization’s Digital Twin is undoubtedly the next step that most companies need to take in terms of EA documentation and method. The subsequent trend that is already emerging is the use of AI on the organization’s digital twin to deepen and accelerate insights generation and scenario modeling.