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.

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Organisational Readiness
Ethics and Governance Readiness
Ethics and Gove…
Business Value Readiness
Business Value…
Infrastructure Readiness
Management Support
Management Sup…
Employee Acceptance of AI
Employee Accepta…
Experimentation Culture
AI Literacy
AI Literacy
AI Talent
AI Talent
ML Infrastructure
ML Infrastruct…
Data Infrastructure
Data Infrastru…
Data Quality
Data Quality
Reference Data
Reference Data
AI Governance
AI Governance
AI Risk Control
AI Risk Cont…
Business Use Case
Business Use…
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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
AI Readiness Index (AIRI) by AI Singapore is licensed under the CC-BY-NC-ND 4.0 (Share and reproduce for non-commercial use only. No rights to modify or create derivatives.)