TOGAF Architecture for Enterprise AI Solutions Training Course

Course overview

Enterprise AI initiatives frequently stall between proof of concept and production because business sponsors, data teams, security specialists and platform owners are working from different assumptions. A customer-service copilot, forecasting model or document-intelligence service may have a promising use case but no agreed target architecture, ownership model, data controls, integration roadmap or decision record. This course equips architects to use TOGAF to turn AI ambitions into governed, traceable enterprise architecture work that can be funded, implemented and operated.

Participants apply the TOGAF Architecture Development Method (ADM) to enterprise AI solutions, from Preliminary and Architecture Vision through implementation governance and architecture change management. They learn to define AI stakeholders and concerns; develop business, data, application and technology architecture views; model AI capabilities, data pipelines, model-serving components and human oversight; and identify risks covering privacy, bias, model drift, security and third-party AI services. The course also connects TOGAF artefacts with ArchiMate viewpoints and the NIST AI Risk Management Framework.

Instruction combines short, focused teaching sessions with a five-day enterprise AI case study. Working in architecture teams, participants produce an AI Architecture Vision, stakeholder map, baseline-to-target architecture, architecture principles, capability increment roadmap, risk treatment decisions and an Architecture Definition Document outline. The final workshop requires each team to defend its target-state AI architecture and implementation sequence to a simulated architecture review board.

The course is suited to professionals who already contribute to enterprise architecture, digital transformation, data platforms or AI delivery and need a repeatable architecture method for scaling AI responsibly. Managers gain staff who can distinguish an attractive AI use case from an implementable enterprise capability, expose dependencies early and create decision-ready architecture deliverables.

Course objectives

By the end of this course, participants will be able to:

• Apply the TOGAF ADM to scope, govern and sequence an enterprise AI architecture engagement
• Produce an AI Architecture Vision with business outcomes, stakeholders, concerns and measurable value hypotheses
• Develop baseline and target Business, Data, Application and Technology Architecture views for an AI solution
• Model AI capabilities, data flows, model lifecycle components and human-in-the-loop controls using ArchiMate viewpoints
• Define architecture principles and standards for data quality, model governance, security and responsible AI use
• Conduct gap analysis and create work packages for AI platform, integration, data and operating-model changes
• Map AI risks to NIST AI RMF functions and record architecture risk treatment decisions
• Create an Implementation and Migration Plan with capability increments, dependencies, governance gates and measurable outcomes

Benefits of attending

Target competencies

Who should attend

• Enterprise Architects — who need to govern AI initiatives across business, data, application and technology domains
• Solution Architects — who must translate AI use cases into integrated, supportable target architectures
• Data Architects — who define trusted data products, lineage and access controls for AI workloads
• AI and Machine Learning Leads — who need to align model delivery with enterprise platforms, controls and roadmaps
• Digital Transformation Managers — who must prioritise AI investments against enterprise capabilities and dependencies
• Technology Risk and Governance Managers — who need traceable architecture controls for responsible AI deployment

Requirements and prerequisites

Participants should understand the purpose of enterprise architecture and have experience contributing to an IT, data, cloud or digital-change initiative. Familiarity with basic concepts such as business capabilities, applications, APIs, data flows, cloud services and information security is assumed. Prior exposure to TOGAF, ArchiMate, machine learning, Python, model training or data science is not required. Participants do not need to configure AI platforms or write code. Complete beginners to enterprise architecture can attend, but should expect to work with architecture terminology, diagrams and structured decision artefacts throughout the week.

Training methodology

The course uses instructor-led architecture walkthroughs, annotated TOGAF ADM templates and a continuous enterprise AI case study. Participants work in small architecture teams to analyse an AI service scenario, define stakeholders and concerns, model baseline and target states in ArchiMate-style views, assess gaps and build implementation increments. Facilitated architecture review board discussions test each team’s assumptions on data governance, model risk, integration and operating ownership. The final session converts the case-study outputs into an individual application plan for a live AI initiative or anticipated organisational need.

Course outline

Day 1: Positioning Enterprise AI with TOGAF

• Enterprise AI solution patterns and architecture failure modes
• TOGAF Standard, 10th Edition structure and Architecture Development Method overview
• Preliminary Phase architecture capability for AI governance
• Architecture principles for responsible, secure and reusable AI services
• Architecture Vision deliverables for AI investment proposals
• Stakeholder maps, concerns and architecture viewpoints
• Business capability mapping for AI-enabled operating models

Workshop: Teams create an AI Architecture Vision, stakeholder map and initial business capability map for a document-intelligence case study.

Day 2: Business and Data Architecture for AI

• Business Architecture for AI-assisted processes and decision rights
• Target operating model roles for model ownership, approval and monitoring
• Data Architecture for training, retrieval, inference and feedback data
• Data product ownership, metadata, lineage and data quality controls
• Privacy classification, consent and retention requirements for AI data
• NIST AI RMF Govern and Map functions in architecture work
• ArchiMate business and data viewpoints for AI services

Workshop: Teams model the target business process, data lifecycle and accountable roles for the case-study AI service.

Day 3: Application and Technology Architecture

• AI application architecture for copilots, predictive models and intelligent automation
• Model lifecycle components including development, evaluation, deployment and monitoring
• Retrieval-augmented generation architecture and vector database integration
• API management, event integration and enterprise system connectivity
• Identity, access management and secrets handling for AI workloads
• Cloud, hybrid and vendor AI platform deployment trade-offs
• ArchiMate application and technology viewpoints for model-serving ecosystems

Workshop: Teams produce baseline and target application and technology architecture diagrams, including AI platform integration points.

Day 4: Risk, Governance and Architecture Change

• NIST AI RMF Measure and Manage functions for architecture decisions
• Threat modelling for prompt injection, data leakage, model abuse and supply-chain exposure
• Bias, explainability, human oversight and escalation control design
• Architecture Repository, reference architectures and reusable AI building blocks
• Architecture Definition Document and Architecture Requirements Specification
• Gap analysis across capabilities, data, applications, platforms and skills
• Architecture governance checkpoints and compliance assessments

Workshop: Teams complete a gap analysis, AI risk register and architecture governance checklist for their target design.

Day 5: Implementation Roadmaps and Architecture Review

• TOGAF Opportunities and Solutions phase for AI transformation
• Work package definition and capability increment planning
• Implementation and Migration Planning dependencies and transition architectures
• Business value, architecture KPIs and AI service success measures
• Implementation Governance contracts and delivery conformance reviews
• Architecture Change Management for model drift, regulation and new AI capabilities
• Architecture review board communication and decision presentation techniques

Workshop: Teams present a target-state AI architecture, migration roadmap and governance decisions to a simulated architecture review board.

Tools & standards covered

TOGAF Standard, 10th Edition, ArchiMate 3.2, Archi, NIST AI Risk Management Framework 1.0

A typical training day

Live online deliveries follow the same structure in the East Africa Time zone, with shorter screen blocks and longer breaks.

What the fee includes

How you can take this course

Certification

Participants who complete the full five days receive the Skillset Development Certificate of Completion, stating the course title, course code, dates and delivery format — suitable for professional-development records and employer reimbursement.

Frequently asked questions