TOGAF Architecture for AI: Applying ADM to Enterprise AI Transformation

How can the TOGAF framework help AI architecture

Artificial Intelligence (AI) is reshaping how organisations work, from automating back-office processes to transforming healthcare, finance, and manufacturing. Yet introducing AI at scale is not just about deploying algorithms—it requires structured planning, governance, and alignment with business goals.

The TOGAF framework, widely recognised for enterprise architecture, offers a robust method for guiding AI adoption. At its heart lies the Architecture Development Method (ADM), a proven, step-by-step process that ensures AI systems are built on solid foundations, managed effectively, and continuously improved.

This article explains how the ADM’s ten phases can be applied to AI and Machine Learning (ML) transformation, turning big ideas into real, trustworthy solutions.

What is TOGAF ADM?

The TOGAF Architecture Development Method is the core of the TOGAF framework. It provides a structured and iterative process for creating, managing, and evolving enterprise architectures.

When applied to AI, ADM helps organisations move from early vision-setting to full-scale AI-enabled operations, while ensuring governance, compliance, and ethical responsibility.

ADM has ten main stages:

1. Preliminary Phase – Prepare the organisation and define AI principles.

2. Architecture Vision – Set the AI strategy and goals.

3. Business Architecture – Redesign processes to use AI effectively.

4. Information Systems Architecture – Build the data and applications to support AI.

5. Technology Architecture – Define the infrastructure for AI workloads.

6. Opportunities & Solutions – Identify AI modules, tools, and platforms.

7. Migration Planning – Create the roadmap for implementation.

8. Implementation Governance – Ensure AI projects align with architecture.

9. Architecture Change Management – Adapt to evolving AI requirements.

10. Requirements Management – Track and validate AI business needs throughout.

Think of it as designing and building a skyscraper: you don’t just hire builders and hope for the best—you define vision, create blueprints, ensure compliance, and manage construction step by step.

Phase 1: Preliminary Phase — Setting the Stage for AI

This phase prepares the organisation for AI adoption. It defines principles, governance, and readiness.

• Purpose: Align AI initiatives with business strategy, ethics, and compliance.

• Actions:

  • Define AI principles (e.g., “AI must be explainable, bias-free, and secure”).

  • Form an AI architecture team and governance board.

  • Assess current AI maturity and skill gaps.

• Outputs:

  • AI architecture principles document.

  • AI governance framework.

  • Training and capability-building plan.

Example: A healthcare network sets up an AI governance board to oversee projects and ensures all AI tools must protect patient privacy.

Phase 2: Architecture Vision — Defining the AI Transformation

This phase builds the high-level case for AI and secures leadership buy-in.

• Purpose: Create a shared AI vision and scope.

• Actions:

  • Identify key stakeholders and their concerns.

  • Define AI goals, such as efficiency gains or cost savings.

  • Build a business case with ROI and risk assessment.

• Outputs:

  • AI adoption charter.

  • Vision and scope document.

  • Stakeholder engagement plan.

Analogy: Like drawing concept art for a building, this stage ensures everyone agrees on the overall direction before work begins.

Phase 3: Business Architecture — Mapping the Business

Here, organisations rethink business processes to integrate AI.

• Purpose: Align AI use cases with strategy.

• Actions:

  • Map current business processes.

  • Identify opportunities for AI (e.g., fraud detection, predictive maintenance).

  • Define future AI-enabled workflows.

• Outputs:

  • AI-enabled business models.

  • Gap analysis report.

  • Prioritised AI use case list.

Example: In healthcare, AI-assisted triage and diagnostics are mapped into patient intake processes.

Phase 4: Information Systems Architecture — Designing the Brains and Memory

This phase defines data and applications for AI.

Data Architecture

• Build pipelines for training, inference, and feedback.

• Define governance for data privacy, bias reduction, and interoperability.

• Plan storage (data lakes, vector databases).

Application Architecture

• Decide how AI applications will be deployed (e.g., cloud platforms, APIs).

• Define integration with existing systems like ERP or CRM.

Example: A hospital moves patient data into a central data lake and integrates AI diagnostics into clinician dashboards.

Phase 5: Technology Architecture — Building the Infrastructure

AI requires powerful infrastructure. This phase sets up compute, storage, and monitoring tools.

• Define cloud, hybrid, or on-premise AI strategy.

• Plan GPU/TPU clusters and edge computing.

• Select observability tools to monitor AI performance and detect drift.

Result: A scalable, secure platform that supports AI/ML workloads across the enterprise.

Phase 6: Opportunities & Solutions — Identifying AI Building Blocks

Organisations now identify AI modules and solutions that deliver the vision.

• Define reusable AI components (e.g., fraud detection module, NLP chatbot).

• Decide which platforms and vendors to use.

• Plan interoperability and scaling.

Phase 7: Migration Planning — Roadmap to AI Adoption

• Create a detailed timeline for AI deployment.

• Balance costs, benefits, and risks.

• Sequence projects into achievable phases.

Example: Deploy AI in finance first for fraud detection, then expand to HR, and finally operations.

Phase 8: Implementation Governance — Ensuring Compliance

This phase ensures AI projects follow the architecture principles.

• Establish review gates for AI projects.

• Monitor adherence to governance and compliance standards.

• Approve or adjust deployments as required.

Phase 9: Architecture Change Management — Adapting to AI Evolution

AI is not static—models evolve, regulations shift, and business needs change.

• Set up processes for continuous monitoring.

• Update architecture in response to new risks, opportunities, or regulations.

• Ensure agility while maintaining compliance.

Phase 10: Requirements Management — Continuous Alignment

Throughout all phases, requirements are tracked and validated. This ensures every AI initiative delivers against business needs and ethical principles.

Conclusion

AI adoption is a complex journey, but with TOGAF ADM, organisations have a clear, repeatable roadmap. By progressing through each phase—vision-setting, business alignment, data design, infrastructure build, and governance—enterprises can ensure AI is trustworthy, scalable, and impactful.

Key takeaways:

• TOGAF ADM provides a structured way to integrate AI into enterprise architecture.

• Governance and ethics are embedded from the start.

• The framework ensures AI projects stay aligned with business goals while adapting to change.