Artificial Intelligence Training Course
Data Readiness for AI
To equip participants with the knowledge and confidence to engage effectively with data, make informed decisions, and lead data-driven organisations.
Course Description
The Data Readiness for AI course helps leaders and business stakeholders understand what it takes for an organisation’s data to support trusted AI, analytics and decision-making at scale. Participants explore why data readiness is a leadership capability, how maturity develops across the organisation, where blockers typically appear, and how stronger data foundations create measurable business value.
This course is designed to help participants assess their current state, identify priority gaps and leave with a clearer view of the actions needed to improve data readiness for AI adoption.
Duration & Delivery Mode
1 Day Virtual or Instructor-led, classroom-based training Facilitated discussion, maturity assessment, group activities, practical reflection and action planning.
Who Should Attend
- Business leaders and senior stakeholders.
- Strategy, operations, technology and transformation teams.
- Data owners, product owners, governance leads and project sponsors supporting AI-enabled initiatives.
- Non-technical decision-makers who need to understand the business implications of data readiness.
Prerequisites
No technical data or AI experience is required. Participants should come prepared to discuss how data is currently used in decision-making, reporting, operations and AI-related initiatives within their organisation.
1 Day Course | Instructor-Led | Classroom-Based
Learning Objectives
By the end of this training, participants will be able to:
- Explain why data readiness is essential for scaling AI and improving decision-making.
- Define data readiness in practical business terms, beyond simply having large volumes of data.
- Describe the core dimensions of data readiness: quality, accessibility, governance, architecture, security and culture.
- Assess current organisational maturity using the data readiness journey from planning to leading.
- Distinguish surface-level symptoms from the root causes of poor data readiness.
- Identify the business impact of low maturity, including decision delays, reporting mistrust, operational inefficiency and AI adoption risk.
- Prioritise practical actions that can improve readiness and support future AI initiatives.
Data Readiness for AI
