Module 1 - AI fundamentals for business adoption
Gain a practical understanding of AI concepts and how organisations can apply AI technologies to create business value.
- Core AI concepts and terminology
- Differences between AI, automation, analytics, and machine learning
- Machine learning, deep learning, generative AI, and AI agents
- Data requirements and dependencies for successful AI adoption
- Common AI limitations, risks, and failure modes
- AI project lifecycles, MLOps, and DataOps fundamentals
- Emerging AI trends and future opportunities
Module 2 - Organisational readiness and AI maturity assessment
Learn how to evaluate organisational preparedness for AI adoption and identify areas requiring improvement.
- AI readiness assessment frameworks
- AI maturity models and benchmarking approaches
- Evaluating people, process, technology, and governance capabilities
- Identifying organisational strengths and gaps
- Assessing adoption risks and barriers
- Building readiness improvement plans
Module 3 - AI use case identification and value prioritisation
Discover how to identify opportunities where AI can deliver measurable business outcomes.
- AI opportunity discovery techniques
- Business value assessment methods
- Feasibility and complexity analysis
- Prioritisation frameworks for AI initiatives
- Return on investment evaluation
- Build versus buy versus partner decision-making approaches
Module 4 - AI strategy and roadmap development
Learn how to translate AI opportunities into actionable strategies and delivery plans.
- Developing AI strategies aligned to organisational objectives
- Defining strategic priorities and success measures
- Building implementation roadmaps
- Dependency mapping and planning
- Designing AI operating models
- Establishing roles, responsibilities, and governance structures
Module 5 - Change management and AI enablement
Explore techniques for supporting organisational adoption and workforce readiness.
- Change management principles for AI transformation
- Applying ADKAR and Kotter frameworks
- Stakeholder engagement strategies
- Building AI awareness and capability programmes
- Developing AI training and enablement plans
- Creating a culture of continuous learning and innovation
Module 6 - AI platforms, tools, and ecosystem
Understand how to evaluate and select AI technologies that align with organisational needs.
- AI platform and tool categories
- Evaluating AI capabilities and business fit
- Vendor assessment and selection criteria
- Security considerations for AI tools
- Vendor governance and maturity assessment
- Integrating AI solutions with enterprise systems
Module 7 - Governance, ethics, and safe AI adoption
Develop the knowledge required to implement responsible and sustainable AI practices.
- AI governance frameworks and operating models
- Policy development and oversight processes
- Ethical AI principles and responsible use
- Bias identification and mitigation approaches
- Compliance and regulatory considerations
- Risk management across the AI lifecycle
Module 8 - AI pilot execution and scaled deployment
Learn how to move AI initiatives from experimentation to organisational adoption.
- Designing AI pilot programmes
- Establishing success criteria and performance metrics
- Deployment readiness planning
- Phased rollout strategies
- Managing operational and adoption risks
- Scaling successful AI initiatives across the organisation
Module 9 - Measuring AI adoption impact and value
Discover how to evaluate AI programme performance and demonstrate business outcomes.
- Adoption measurement frameworks
- Tracking capability development and workforce readiness
- Defining key performance indicators
- Quantifying business value and return on investment
- Executive reporting and dashboard design
- Communicating programme success to stakeholders
Module 10 - Sustaining AI transformation and continuous improvement
Build the foundations for long-term AI success within the organisation.
- Continuous improvement practices
- Monitoring emerging technologies and opportunities
- Maintaining governance and oversight
- Leadership responsibilities in AI transformation
- Building a sustainable AI culture
- Evolving AI strategies to meet changing business needs
Exams and assessments
This course includes practical exercises, facilitated discussions, scenario-based activities, and knowledge checks throughout the programme.
Learners will complete hands-on activities focused on AI readiness assessment, use case prioritisation, governance planning, AI tool evaluation, and value measurement.
The Certified AI Program Manager (CAIPM) certification exam is taken after the course. An exam voucher is included with attendance.
Hands-on learning
This course includes practical exercises designed to reinforce key concepts and provide real-world application opportunities.
Hands-on activities include:
- Enterprise AI readiness and maturity assessment
- AI use case discovery and prioritisation
- AI strategy and roadmap development
- Change management and workforce enablement planning
- AI tool evaluation and selection
- Responsible AI governance and risk management
- AI pilot execution and scale decision-making
- AI value measurement and reporting
- Sustaining enterprise AI transformation
