Course Objective & Scope
The biggest obstacle to AI investment is rarely the model; it is scattered data, unclear ownership, missing skills and governance gaps. This programme gives the organisation a six-dimension maturity model to see objectively where it starts, and runs the assessment together with participants.
Using interview guides, survey templates and data readiness checklists, units are scored and results turned into a heat map. The work ends with an improvement roadmap of initiatives that close priority gaps, plus a draft maturity report for management.
Who Should Attend?
- Digital transformation and strategy office teams
- Data, IT and enterprise architecture managers
- Risk, compliance and internal audit representatives
- Teams setting up or running an AI Center of Excellence
Key Takeaways
- Adapt a six-dimension AI maturity model to the organisation's structure
- Score maturity by unit using interviews and surveys
- Assess data readiness in terms of quality, accessibility and ownership
- Prioritise gaps by impact and urgency
- Draft the maturity report and improvement roadmap
Curriculum
AI Maturity Models and the Assessment Framework
- Strategy, data, technology, talent, governance and culture dimensions
- Maturity levels and evidence criteria
Data and Technology Readiness
- Data quality, access, catalogue and ownership checks
- Reviewing cloud, platform and integration infrastructure
- Data usability from a security and KVKK perspective
People, Skills and Culture Readiness
- AI literacy and technical skills inventory
- Indicators of openness to change and experimentation culture
Governance and Risk Readiness
- AI policy, approval processes and role definitions
- Preliminary gap analysis against EU AI Act and ISO/IEC 42001
Running the Assessment: Interviews, Surveys and Scoring
- Adapting interview guides and survey templates
- Unit-level scoring and maturity heat map
- Calibrating and validating findings
Gap Analysis and Improvement Roadmap
- Ranking gaps by impact and urgency
- Initiatives, ownership and dependencies
- Structuring the maturity report for management
Other Courses in This Category
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For managers running AI initiatives: the experimental, data-dependent nature of AI projects, CRISP-DM and PoC–pilot–production stages, success criteria, team roles, AI-specific risks and vendor management.
- Live Course
- 5 modules
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What managers need to know about MLOps and LLMOps to move models from pilot to reliable service: lifecycle, monitoring and drift, evals, cost, platform options (MLflow, Azure AI Foundry, Bedrock, Vertex AI) and team structure.
- Live Course
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For business unit and strategy leaders: discovering AI opportunities across the value chain, scoring them on value, feasibility and risk, and building a balanced use case portfolio with prioritised use case cards.
- Live Course
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For finance, strategy and business leaders: measuring financial and operational returns of AI investments, covering TCO, token and infrastructure costs, productivity-to-P&L conversion, baselines and an ROI business case template.
- Live Course
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For executives, architects and procurement leaders: evaluating in-house build, SaaS buy and strategic partner options for AI solutions across competitive differentiation, TCO, speed to market and vendor lock-in.
- Live Course
- 5 modules
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For strategy, PMO and transformation leaders: turning a use case portfolio into an executable three-horizon (90-day, 6-month, 18-month) enterprise AI roadmap aligned with data, platform and organisational dependencies.
- Live Course
- 4 modules
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