Artificial Intelligence is often portrayed as a disruptive force — one that demands new methods, tools, and frameworks. But in practice, AI doesn’t replace traditional architecture frameworks. Instead, it enhances them.

This is especially true when it comes to the TOGAF Standard’s Architecture Development Method (ADM). Rather than rewriting the playbook, AI can act as a force multiplier for each phase of the ADM. It injects speed, insight, automation, and adaptability into an already well-established approach — while leaving room for the human judgment and strategic thinking that make Enterprise Architecture effective in the first place.

In this post, we’ll explore how AI supports and strengthens each phase of the ADM, helping organizations turn architectural intent into scalable, data-driven execution. In other words: architecting with AI.

Setting the Stage: The Preliminary Phase

The ADM begins with establishing the architecture capability and defining principles. Traditionally, this phase is heavy on documentation and stakeholder alignment.

With AI, this foundational work becomes more dynamic. Natural language models can rapidly scan and synthesize architectural documentation, policy repositories, and audit histories to highlight recurring pain points. These insights help architecture teams define principles grounded in real-world operational experience — not just best-practice templates.

AI tools also support capability assessment. Skills inventories, role descriptions, and training records can be analyzed to identify gaps in the team’s ability to support AI initiatives, from data stewardship to machine learning governance.

The result: a starting point for architecture that is better informed, risk-aware, and aligned with the actual operating environment.

Phase A: Creating an Actionable Architecture Vision

Visioning in the ADM isn’t just about setting direction — it’s about establishing a credible case for change.

AI can strengthen this process in two key ways. First, generative models can simulate business scenarios based on real enterprise data. For example, AI can model the operational and financial impact of implementing predictive maintenance, intelligent customer service, or algorithmic procurement. These scenario outputs make business cases more compelling — and less speculative.

Second, AI tools can analyze stakeholder communication (emails, surveys, meeting notes) to identify sentiment trends and key concerns. This allows architecture teams to tailor the vision to what stakeholders care about, increasing alignment and support.

Together, these capabilities help organizations move from abstract ambitions to data-backed strategic clarity.

Phase B: Rethinking Business Architecture with AI Assistance

Business architecture work often struggles with fidelity: what’s modeled rarely matches reality.

AI addresses this gap by enabling process discovery through data. Tools can ingest workflow logs, screen interactions, and system traces to automatically map how business processes actually work — not how they are documented.

These real-world models make it easier to identify inefficiencies, bottlenecks, and opportunities for AI enablement. For example, repetitive manual steps in procurement or onboarding processes may be ideal candidates for intelligent automation.

AI systems can semantically analyze existing capability models, clustering them to expose overlaps or gaps. This leads to a more refined business architecture that supports both current-state understanding and future-state innovation.

Phase C: Building a Data-Driven Information Systems Architecture

When AI becomes part of the solution space, data architecture moves to the forefront.

AI tools assist by automatically profiling data sources — structured and unstructured — to assess their readiness for machine learning and analytics use cases. This includes evaluating data quality, lineage, sensitivity (especially for privacy), and volume trends.

Crucially, AI also helps detect bias in datasets, an important requirement for responsible AI implementation. Whether it’s skewed demographics or missing values, these insights are essential for architects working in regulated or high-stakes environments.

On the application side, AI models can recommend integration points for new capabilities. For instance, embedding an AI-based forecasting engine into an existing ERP can be mapped out using tools that understand service boundaries and data flows.

The result is an Information Systems Architecture that is AI-aware, technically sound, and regulation-ready.

Phase D: Designing the Technology Architecture for AI Workloads

AI workloads come with infrastructure challenges: specialized hardware, flexible compute, and large-scale storage.

In this phase, AI tools help architects simulate various deployment architectures and predict performance characteristics. This is especially useful in balancing on-premise and cloud strategies or designing hybrid environments.

AI-driven capacity planning and cost-optimization engines also provide real-time feedback on resource utilization, helping teams avoid overprovisioning and unnecessary spend.

Moreover, MLOps platforms — powered by AI — enable continuous integration and deployment of machine learning models. They also automate governance controls, such as monitoring for data drift, ensuring model explainability, and enforcing compliance.

These capabilities ensure that the technology architecture is not just scalable and resilient, but also optimized for the evolving nature of AI workloads.

Phase E: Evaluating Solutions and Structuring Delivery

At this point, AI becomes instrumental in evaluating and selecting the right architectural solutions.

Use-case prioritization — often mired in politics or intuition — can now be supported with AI-powered scoring models. These models assess feasibility, ROI, risk, and stakeholder alignment based on historical data and domain-specific criteria.

Once solutions are selected, AI design assistants can generate architecture artifacts: draft diagrams, interaction flows, and even pseudo-code. While human review is still essential, this dramatically reduces the time required to prepare solution documentation.

By augmenting this phase with AI, architecture teams can move faster from intent to implementation, without sacrificing quality or governance.

Phase F: Planning the Migration with Intelligence

Migration planning has always been difficult: dependencies are unclear, assumptions change, and sequencing is complex.

AI supports this by creating and continuously refining dependency graphs. These graphs reflect system interconnections, change risks, and stakeholder constraints — making it easier to sequence migrations intelligently.

Scenario planning tools also simulate different roadmap paths. For instance, what happens if a core system decommission is delayed? How would a regulatory change impact the timeline?

With AI providing these what-if analyses, architecture teams can create resilient, adaptable migration plans that respond to uncertainty and complexity.

Phase G: Implementing Governance at the Speed of Change

AI doesn’t just generate solutions — it can help govern them.

During implementation, AI models can monitor project progress, detect misalignments with architecture specifications, and flag issues with data usage or system design. These models operate in near real-time, integrating with project management tools.

Architecture compliance reviews — often manual and delayed — become continuous and intelligent. AI tools can parse infrastructure-as-code, APIs, and logs to verify adherence to architectural principles.

For organizations deploying AI solutions themselves, AI-enabled model governance ensures that deployed models continue to perform within acceptable bounds — including accuracy, fairness, and reliability.

This phase becomes a living governance layer, not a box-checking exercise.

Phase H: Managing Change in an AI-Accelerated World

Enterprise environments are in constant motion. AI helps architecture teams keep up and stay ahead.

Change signals come from everywhere: evolving regulations, new technologies, performance data, and customer feedback. AI systems can monitor these sources and surface emerging trends, risks, or opportunities.

For instance, changes in the AI regulatory landscape (e.g., the EU AI Act) can be detected, summarized, and flagged to architecture teams, complete with recommended policy adjustments.

Feedback from users of AI-enabled systems can also be analyzed at scale, helping identify areas where models underperform or where expectations are not met.

In this phase, AI supports continuous architecture evolution, rather than periodic reviews.

The Bigger Impact

Applying AI to the ADM is not about cutting corners — it’s about elevating the practice of Enterprise Architecture.

AI reduces friction in documentation, discovery, planning, and governance. It accelerates execution without losing structure. And it brings data-driven confidence to a discipline that often struggles to prove its impact.

More importantly, it helps architects stay relevant in a world where change is constant and complexity is the norm.

The TOGAF Standard’s ADM, when enhanced by AI, becomes more than an approach to developing architectures — it becomes a living, learning system for organization-wide change.

Closing Thoughts

AI will not replace Enterprise Architects. But architects who leverage AI — thoughtfully, ethically, and strategically — will replace those who don’t.

By embracing AI as a collaborator within the TOGAF ADM, organizations gain a practical path to modernization, transformation, and competitive advantage. The methodology remains the same. The difference lies in how intelligently, quickly, and adaptively it can now be applied. And that is the real power of bringing AI into the architectural fold.