AI READINESS INDEX
Assess AI Readiness. Identify Gaps. Accelerate AI Adoption.
The AI Readiness Index
AI Readiness Index (AIRI) is an industry-focused AI readiness assessment framework developed by AI Singapore (AISG). It crystallises and distils the critical success factors for AI adoption based on hundreds of engagements AISG has with companies across different industries, sizes, and AI readiness.
AIRI allows business units and organisations to assess their AI readiness and identify the gap between their current and desired state, thereby enabling organisations to understand their suitable approaches to adopt AI and implement targeted programmes to increase AI readiness.
Ultimately, AIRI translates abstract concepts into concrete actions to help organisations accelerate their AI adoptions.
In fact, AI Singapore uses AIRI as a tool to help our companies identify which programmes can be leveraged to accelerate their AI journey.
| AI Unaware | AI Aware | AI Ready | AI Competent | |
|---|---|---|---|---|
| Average Score | Less than 2.5 | 2.5 to 3.4 | 3.5 to 4.5 | More than 4.5 |
| General Capabilities | Might hear about AI but is unaware of applications | Savvy consumers of AI solutions. Capable of identifying use cases for AI applications | Capable of integrating pre-trained AI model into products or business processes | Capable of developing customized AI solutions for specific business needs |
| General Characteristics | Wait for vendors to convince use cases and business value of AI | Identified potential use cases and seek AI solutions from vendors | Evaluated viability of pre-trained AI models | Developed roadmap for AI implementation |
| AI Adoption Suitability | Consume ready-made, end-to-end AI solutions | Integrate pre-trained AI models and solutions for common AI applications | Develop customized AI model for unique business needs | |
AIRI Components
AIRI consists of five pillars, which map to twelve dimensions. The five pillars are interdependent and synergistic.
Organisations with strong Organisational Readiness could identify good use cases, thereby contributing to Business Value Readiness. The decision and approach of identifying appropriate Business Use Case is guided by Ethics and Governance Readiness. The use cases are supported by Data Readiness with established data policies, processes, and practices to ensure accuracy, reliability, and completeness of data. Infrastructure Readiness helps to turn ideas into actions by providing the organization with the tools and technologies to train, host, and deploy AI solutions.
Collectively, the five main pillars of AIRI provide a holistic assessment of an organisation’s readiness to adopt AI.
5 Pillars and 12 Dimensions of AIRI
The five pillars and twelve dimensions assess a specific area that contributes to the overall AI readiness of organisations.
In each pillar, it has several dimensions and each dimension is assessed at four levels of AI Readiness:
- AI Unaware
- AI Aware
- AI Ready
- AI Competent
Organisations can exhibit different levels of AI Readiness across the dimensions.
| Pillars | Dimensions | Assessments |
|---|---|---|
| Organisational Readiness | Management Support | Whether the organisation has allocated resources for AI initiatives |
| AI Literacy | Whether the employees could identify potential AI use cases and be savvy consumers of AI solutions | |
| AI Talent | Whether the organisation has the capabilities to develop, integrate, and maintain AI models | |
| Employee Acceptance of AI | Whether the employees trust and accept AI-bases systems | |
| Experimentation Culture | Whether the organisation has an experimentation culture for employees to explore and develop AI use cases | |
| Ethics and Governance Readiness | AI Governance | Whether the organisation has appropriate governance to avoid unintentionally harming end-users |
| AI Risk Control | Whether the organisation has a proper classification of the risk level of AI systemss | |
| Business Value Readiness | Business Use Case | Whether the organisation has identified suitable AI use cases and assessed their value propositions |
| Data Readiness | Data Quality | Whether the organisation has processes to ensure the quality (accuracy, completeness) of data collecteds |
| Reference Data | Whether there is a single source of truth, consistency of data format, and reliable metadata | |
| Infrastructure Readiness | Machine Learning (ML) Infrastructure | Whether the organisation has appropriate and sufficient ML infrastructure (e.g., GPU, memory) to support AI model training and deployment |
| Data Infrastructure | Whether the organisation is using appropriate data infrastructure (e.g., data lake) as a central repository of data |
