Why AI Must Be on Your Enterprise Architecture Radar

Artificial Intelligence (AI) has moved beyond the stage of being a promising technology. Many organizations already make it a core part of their digital transformation plans. As AI transitions from standalone experiments to organization-wide initiatives, the demand for effective decision-making and robust risk management is increasing.

Enterprise architects are in a unique position to lead this transition. By integrating AI into the organization’s enterprise architecture across capabilities, processes,  applications, and governance structures, enterprise architects help organizations move beyond experimentation toward scalable, value-driven execution. To help you navigate this shift, this blog offers practical guidance for embedding AI in your enterprise architecture.

The early phase of AI was characterized by experimentation and exploration. Now, AI services are more mature and are being broadly adopted. Product vendors are integrating AI-based features or even full components into their tools, and organizations are increasingly adopting them in practice. As we move into this adoption phase, scaling AI must be intentional, aligned, and governed. The key question becomes: “Where should we use AI, and how do we scale it safely and effectively?”

Enterprise Architecture (EA)  becomes essential at this stage. It provides the structure and context needed to move beyond isolated pilots and turn AI into a stable, organization-wide capability.

AI opens up new ways for organizations to deliver value to customers and optimize internal operations.

Examples include:

• Enhancing customer service with AI-powered chat and email assistants that learn from past interactions 

• Supporting operational teams with AI-driven recommendations based on real-time data patterns 

• Enabling faster onboarding and training through dynamic, conversational learning interfaces 

• Accelerating report creation and market analysis with automatically generated insights 

• Automating routine documentation like incident logs, shift reports, and maintenance summaries

Such AI-driven solutions deliver the most value when closely tied to specific business priorities. To achieve measurable results, organizations need reliable data, teams who can work confidently with Al, and well-defined rules that ensure responsible and compliant use.

When AI is embedded in core business capabilities and applications, it becomes a lever for strategic value. Enterprise architects play a key role in shaping this integration to ensure AI delivers outcomes that matter.

Enterprise Architecture guides how AI is introduced and scaled across the organization. As companies move from basic chatbot experiments to more advanced agents and orchestration tools, EA becomes essential in selecting the right implementation model for each case. This includes understanding the benefits and trade-offs of different approaches, ensuring appropriate oversight for each use case, and supporting long-term adaptability.

There are several technical and organizational patterns to implement AI:

• Direct Use of Public Tools
  Fast to adopt and ideal for early ideation, but often unsuitable for regulated industries or sensitive processes.
• API-Based Integration
  Enables embedding AI capabilities into existing platforms and user interfaces, using secure service connections.
• Custom or Fine-Tuned Models
  Offers the greatest control and domain-specific relevance, but requires significant investment in infrastructure, data, and model governance.
• Agentic AI Systems
  Architected solutions where autonomous or semi-autonomous agents plan, reason, and act on behalf of users using large language models (LLMs). These agents can sense environments, call functions, and coordinate with other agents.

Enterprise architects decide which approaches suit each situation. They also build processes to monitor results and address risks as AI is put into practice.

To ensure AI is implemented in a way that supports long-term scalability and business alignment, the following architectural principles and patterns should be considered:

1. Align AI with business purpose
AI components shouldn’t exist in isolation. Architects should connect them to Business Capabilities and Value Streams to clarify their role in delivering business outcomes. This reinforces strategic alignment and avoids fragmented experimentation.

2. Reflect AI services in the application layer
LLM-based agents and APIs should be explicitly modelled as Application Services within the architecture. These services can then be embedded into Application Components to show how they support or enhance specific operational capabilities.

3. Use implementation patterns based on complexity and control
Depending on the use case, architects can adopt different agent-based patterns, such as:

• • Solo agents for contained, task-specific automation 
  • Multi-agent orchestration for complex workflows 
  • Human-in-the-loop agents for scenarios requiring oversight or final validation

To tailor an architecture solution to the organization’s risk tolerance, process complexity, and maturity level, consider the following:

1. Design for modularity and adaptability
Agentic AI architectures benefit from a modular design, where each agent or service can be developed, tested, and evolved independently. This matches EA principles like loose coupling, controlled change, and reuse, which help contain complexity as systems grow.

2. Model governance safeguards explicitly
To ensure responsible use of AI, architecture models should include:

• • AI guardrails that prevent unsafe or off-topic behavior 
  • Structured response validation to ensure output accuracy and relevance 
  • Identity token propagation for tracking ownership, permissions, and audit trails across automated steps

ADOIT offers integrated AI capabilities designed to support enterprise architects in their daily work, from initial ideation to scalable execution.

With ADOIT, enterprise architects can confidently harness AI to:

• Create strategic requirements in capability-based roadmapping by analysing and interpreting interlinked repository data, such as assigned goals, identified gaps, and transformation needs, associated with each capability.

• Analyse business fit and IT fit of applications and propose investment strategies based on time-based criteria – such as tolerate, invest, migrate, or eliminate. 

• Support application roadmapping by generating data-driven recommendations for application evolution, aligned with fitness assessments, strategic priorities, and investment strategies. 

• Analyse architecture diagrams and receive intelligent feedback, whether related to modelling conventions or architectural content quality.

• Retrieve end-of-life dates for system software elements to proactively manage technical risk and support informed planning decisions

Built on open standards like ArchiMate and capability-based planning, ADOIT empowers architects to drive AI initiatives with structure, transparency, and confidence.

AI can augment many aspects of architecture work, from producing documentation to analysing application landscapes and simulating changes. Specifically, architects can use AI to:

• Draft architecture documentation and reports 

• Summarize stakeholder inputs and decisions 

• Identify redundant applications and suggest improvements 

• Explore scenarios and simulate architecture changes 

Through this support, enterprise architects become more strategic, focusing their time on high-value guidance rather than repetitive documentation tasks.

Capability Map of an airport in ADOIT

To lead AI adoption effectively, enterprise architects should follow a structured roadmap. This step-by-step approach ensures architectural traceability and sustainable delivery.

Develop viewpoints and views to articulate AI’s potential and limitations. Relate them to business drivers, goals, and assessments to guide stakeholder conversations.

Translate goals and drivers into a course of action. Align AI opportunities with existing capabilities and value streams, and connect them to measurable business outcomes. 

Represent AI use cases as strategic requirements linked to business capabilities and application components. Use capability-based planning to evaluate them. Enterprise architects should assess capability by capability to identify AI potential. When needed, model the inner workings, such as the related business roles, processes, data objects, application components, equipment, and system software.

Operating model of an airports ‘Passenger Processing’ capability

A targeted evaluation of AI potential starts by examining each business capability through questions like:

• Which business roles or actors perform manual, repetitive, or decision-intensive tasks that could benefit from AI support? 

• Are there business processes that involve large volumes of content, decisions, or exceptions that AI could help automate or streamline? 

• Does the capability handle unstructured or scattered data (e.g., emails, documents, logs) that AI could help organize, summarize, or make searchable? 

• Can existing application components be extended with AI APIs or services to provide new functionality or improved user interaction? 

• Are there devices, equipment or technology services that could be monitored, configured, or interpreted more intelligently through AI interfaces or agents? 

These questions provide a basis for a structured analysis of AI fit and impact, grounded in architectural modelling best practices. 

Define business policies, constraints, and controls. Link them to business roles, data objects, and application components to ensure responsible, accountable AI implementation. 

Develop a roadmap to guide phased AI delivery across prioritized business capabilities. To this end, EAs can leverage ADOIT’s capability-based roadmapping workspaces.

Hint: Learn more about creating effective roadmaps in our blog.

As AI capabilities, regulations, and business needs evolve, so must your architecture. Regularly review and update your capability prioritizations and their inner workings – such as the related processes, roles, applications, and data. Adjust views to reflect new gaps and opportunities, and adapt your roadmaps accordingly.

AI is becoming a core force in digital strategy. Enterprise architects are uniquely equipped to ensure this force is directed with purpose, structure, and control. 

With the right architectural frameworks and platforms like ADOIT, architects can lead their organizations from experimentation to execution, ensuring that AI becomes a strategic enabler, not a disconnected initiative.