AI Strategy & Governance Foundation and Practitioner
The course covers AI ROI and value mapping, global AI regulation, intellectual property and data risks, change management, workforce redesign, AI procurement, governance committee design, and the creation of a practical 12-month AI implementation roadmap.
Description
Our AI Strategy & Governance Foundation and Practitioner Course in United Kingdom helps professionals understand how to plan, govern, and implement AI initiatives at an organisational level. This is a practical AI Strategy Course for senior professionals to assess AI ROI, navigate global regulation, manage change aligning with business objectives.
- Design AI strategy aligned to business goals, value creation, and risk appetite
- Build structured governance frameworks for responsible and ethical AI adoption
- Manage AI compliance, oversight, and enterprise-wide change effectively
About This Course
Module 1: AI ROI & Opportunity Matrix
- AI Value Mapping
- Enterprise AI Opportunity Identification Framework
- Value-Chain Mapping Exercise
- AI Use-Case Classification (Automation, Augmentation, Transformation)
- AI Maturity Scoring across Departments
- Prioritisation Matrix Design
- Identifying Low-Hanging Fruit: Efficiency and Cost Reduction
- Process Benchmarking Methodology
- Cost-Baseline Modelling before AI Deployment
- Productivity Uplift Estimation Methods
- Risk-Adjusted Savings Calculations
- Quick-win Implementation Scoring
- Defining Moonshots: Revenue Innovation and Competitive Differentiation
- AI-Enabled Business Model Innovation
- Product and Service Redesign using AI
- Competitive Advantage Modelling
- Market Differentiation through Proprietary Data
- Innovation Risk Analysis
- True Cost of AI
- Total Cost of Ownership (TCO) Framework
- Infrastructure and Integration Cost Modelling
- Governance and Compliance Overhead Costs
- AI Lifecycle Cost Planning
- Failure and Rollback Cost Scenarios
- Calculating API and Compute Token Costs
- Consumption Modelling Scenarios
- Usage Forecasting Methods
- Scaling Cost Sensitivity Analysis
- Budget Control Thresholds
- Enterprise Cost Governance Controls
- Understanding the Hidden Human Cost of Training and Maintenance
- AI Supervision and Oversight Requirements
- Model Retraining and Monitoring Cycles
- Workforce Change Management Investment
- Governance Resourcing Models
- Internal Capability Development Planning
- Practical Lab: The ROI Audit
- Using the ROI Calculator Across Five Business Departments
- Comparing 12-Month Return Projections
- Prioritising Departments for Initial AI Investment
Module 2: Risk, Security & The Law
- Global Regulatory Landscape
- Comparative AI Governance Models
- Risk-based AI Classification Systems
- Enforcement Trends and Penalties
- Sector-Specific Compliance Implications
- Regulatory Horizon Scanning
- EU AI Act
- High-risk AI System Obligations
- Conformity Assessment Processes
- Transparency and Documentation Requirements
- Governance Accountability Mapping
- Board-level Reporting Implications
- UK AI Safety Framework
- Pro-Innovation Regulatory Principles
- Safety-by-Design Expectations
- Accountability and Transparency Mechanisms
- Regulatory Reporting Expectations
- US Executive Orders and Federal Guidance
- Federal Oversight Priorities
- Procurement Implications
- Responsible AI Policy Requirements
- Risk Disclosure Expectations
- Intellectual Property in the Age of AI
- Ownership of AI-Generated Output
- Contractual Ownership Allocation
- Licensing Risk Mitigation
- Commercial Exploitation Safeguards
- Copyright Considerations and Data Leakage Risks
- Training Data Exposure Risks
- Prompt Confidentiality Controls
- Internal Data Classification Policies
- Data Retention and Audit Trails
- Practical Lab: The Risk Register
- Identifying the Top Three AI Risks by Industry
- Designing a Mitigation Strategy for Each Risk
- Building a Repeatable Risk Assessment Framework
Module 3: The Human Element & Change Management
- Navigating AI Anxiety
- Organisational Resistance Mapping
- Trust-Building Leadership Models
- Communication Sequencing Strategy
- Psychological Safety Principles
- Addressing the Job Displacement Narrative
- Automation Impact Forecasting
- Workforce Transition Risk Modelling
- Ethical Restructuring Principles
- Reputation Risk Management
- Reskilling vs. Upskilling: Designing the Right Response
- Enterprise Skills Gap Analysis
- AI Capability Maturity Framework
- Learning Pathway Development
- Budget Allocation for Workforce Transformation
- Organisational Design for an AI-Augmented Workforce
- Restructuring Roles When Entry-Level Work Is Automated
- Job Architecture Redesign
- Governance Oversight Roles
- Decision Authority Reallocation
- Role Accountability Mapping
- Building Human-AI Collaborative Team Structures
- Human-in-the-Loop Governance
- Decision Augmentation Frameworks
- Escalation Design
- Performance Measurement Alignment
- Practical Lab: The Town Hall
- Drafting a CEO Internal Memo on AI Adoption
- Writing an AI Ethics Manifesto
- Balancing Corporate Ambition With Employee Psychological Safety
Module 4: The 12-Month AI Roadmap
- Procurement Frameworks
- Build vs Buy vs Partner Evaluation Model
- Vendor Scoring Matrix
- Financial Comparison Modelling
- Risk-Adjusted Procurement Decision-Making
- Evaluating and Selecting AI Vendors
- Technical Due Diligence Checklist
- Data Security Validation Steps
- Scalability and Interoperability Assessment
- Contractual Risk Review
- SOC2 Compliance and Technical Due Diligence
- Security Control Verification
- Audit Documentation Review
- Data Governance Validation
- Compliance Gap Assessment
- AI Governance Committees
- Establishing an Internal AI Council
- Governance Charter Development
- Stakeholder Representation Model
- Reporting Cadence Design
- Defining Roles, Responsibilities, and Escalation Pathways
- RACI Matrix Development
- Risk Escalation Workflows
- Incident Response Governance
- Board Reporting Structure
- Practical Lab: The Roadmap Pitch
- Building a Four-Quarter AI Implementation Roadmap
- Setting Budgets and Defining KPIs
- Designing a Day-One Pilot Programme
Assessment
To achieve the AI Strategy & Governance Foundation and Practitioner Certification, candidates will need to sit for an examination that evaluates their understanding of AI adoption at an organisational level, including governance, risk management, compliance, and value realisation. The exam tests the ability to align AI initiatives with business strategy, establish responsible AI frameworks, and lead sustainable AI transformation. The exam format is as follows:
- Question Type: Multiple Choice
- Total Questions: 50
- Total Marks: 50 Marks
- Pass Mark: 74%, or 37/50 Marks
Prerequisites
There are no formal prerequisites for attending this AI Strategy & Governance Foundation and Practitioner Course. However, familiarity with business operations, digital technologies, or organisational strategy will help delegates engage more fully with the course content. Prior exposure to AI tools or productivity platforms is beneficial but not required.
Who Should Attend?
This AI Strategy & Governance Foundation and Practitioner Course equips delegates with the strategic insight and governance capability required to shape, approve, and implement responsible AI initiatives at an organisational level. This training can benefit a wide range of professionals, including:
- Senior Leaders
- Board Members
- Chief Technology Officers
- Chief Data Officers
- Risk and Compliance Professionals
- Digital Transformation Leaders
- Strategy Consultants
What's Included?
- World-Class Training Sessions from Experienced Instructors
- AI Strategy & Governance Foundation and Practitioner Examination
- AI Strategy & Governance Foundation and Practitioner Certificate
- Digital Delegate Pack
You’ll also get access to the MyTKA Training Portal, which will be your go to hub for all your training.
Hands-On Labs: Included as part of our online instructor-led delivery, these labs provide real-world exercises in a simulated environment guided by expert instructors to enhance your practical skills.