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.
Assess. Identify. Accelerate
AIRI allows business units and organisations to assess their AI readiness and identify the gap between their current and desired state. It enables 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.
AI Singapore uses AIRI to help our companies identify which of our programmes (and our collaborator’s 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 | Greater than 4.5 | ||||
| Interpretation | Organisation might hear about AI, but is unaware of AI applications. | Organisation is aware of AI applications and could identify potential use cases. | Organisation has the capabilities to integrate pre-trained AI models into products or business processes. | Organisation has the capabilities to develop customised AI models and solutions for specific business needs. | ||||
| Characteristics | Wait for vendors to convince with use cases and business value of AI Consume ready-made AI solutions | Actively seek AI solutions to address business needs. Able to identified potential use cases for AI applications, and consume ready-made AI solutions. | Evaluate and seek AI APIs, SDKs and pre-trained AI models for use within business. | Have strategy and roadmap of AI deployment for organisation. | ||||
| Recommendation | Increase AI literacy of organisation. | Consume ready-made, end-to-end, COTS AI solutions. | Prepare organisation to adopt and integrate AI solution. Broaden understanding of AI to whole of organisation. | Deepen organisational AI capabilities. Broaden understanding of AI to whole of organisation. | ||||
| AI Singapore | ||||||||
| Advisory and Roadmaps | AI Advisory Programmes: Discovery Workshops, AI Ready Clinics for Leaders | |||||||
| Skills and Training | AI For Everyone (AI4E), Data Analytics For Everyone | |||||||
| LearnAI: AI For Industry (AI4I), AIAP Field Guide | ||||||||
| AI Certification | ||||||||
| Capabilities | AI Apprenticeship Programme (AIAP), LLM Application Developer Programme (LADP) | |||||||
| Innovation | 100Experiments (100E) | |||||||
| Data and Tools | Makerspace Bricks | |||||||
targeted intervention based on airi
Companies keen to collaborate with AI Singapore and list their programmes according to AIRI, please contact us.
5 Pillars and 12 Dimensions of AIRI
AIRI (version 2.0) 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 Cases are 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 organisation with the tools and technologies to train, host, and deploy AI solutions.
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.
Collectively, the 5 main pillars of AIRI provide a holistic assessment of an organisation’s readiness to adopt AI.
5 pillars and 12 dimensions of airi
| 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 systems | |
| 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 collected |
| 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 |
airi assessment table
becoming ai Aware
AIRI Insights
It is a common misconception that AI adoption is only suitable for larger or technology-based organisations. On the contrary, smaller AI Unaware and AI Aware organisations, even if they lack data, talent, or ML infrastructure could adopt ready-made AI solutions for their core or peripheral business activities.
Organisations should assess if their current AI capabilities support their organisational objectives. If there is a mismatch, organisations could refer to their organisational capabilities’ profiles as a high-level guide on specific areas to target and improve.
An important point to note is that not every organisation needs to reach AI Competent.
The ideal AI readiness is dependent on the organisational objectives.
Nonetheless, given the pervasiveness of AI technology, organisations should minimally aspire to be AI Aware. This enables them to identify better use cases for AI, procure relevant AI solutions, and be savvy consumers of AI.
Unaware2Aware
Beyond increasing organisational AI literacy, the organisation sets a budget for its first AI project and identifies AI champions to onboard their first AI use case using the off-the-shelf AI products and solutions.
Ready2Competent
The organisation expands its AI project team to a full fledge AI Engineering team that can undertake and implement multiple AI projects internally.
Aware2Ready
The organisation assembles an AI project team that can work with stakeholders to identify a good AI use case for the business and implement the solution, possibly with an external trusted AI solution provider.
AI Competent
AI Competent organisations typically can develop customised solutions for unique business needs when none are available in the market. They are only limited by their imaginations, data, and resources on the type of AI solutions they could develop.
Approaches to Improving AI Readiness
Below are some suggested approaches for organisations to benefit from the AIRI assessment and improve their AI readiness:
Determine whether current AI capability supports organisational goals
AI, similar to other technologies, is a tool that could help organisations increase their competitiveness via higher automation (cost-saving), better product offerings (revenue), or deeper analytics capabilities (insights).
Organisational goals serve as the north star for technology adoption; adopting AI without a clear direction and purpose will bring disappointing results. Therefore, organisations should work backwards from their organisational goals to identify potential areas where AI could add exponential value before investing in or further into it.
Once the potential areas of AI applications are identified, organisations could decide whether the use cases justify hiring a team of AI Engineers to develop customised solutions. Not every use case require customised solutions; organisations, especially AI Unaware and AI Aware, should first look at commercially available solution before developing their own AI solutions in-house.
For instance, a law firm could procure a commercially available chatbot solution to support its customer service activities. Such an approach is quicker, has lower risk, and will let the organisation gain experience using AI applications.
Organisations should also consider whether having a specific AI application is regarded as a core competitive advantage. For example, if the law firm believes AI-powered law case review is a core competitive advantage or there is none available in the market, there is a greater incentive to create such a solution in-house.
Identify which level of AI capabilities the organisation needs to be at
Organisations could refer to the Interpretation of AIRI results to understand which AI capabilities they need to be at. Generally speaking, organisations looking to adopt commercially available solutions could be AI Unaware or AI Aware. Organisations looking to integrate AI features, such as AI services from cloud providers, into their products should be AI Ready. Finally, organisations looking to develop their customised AI solution should be at AI Competent level.
Focus on the weakest dimension first
Organisations looking to improve their AI readiness should focus first on their weakest dimension based on their Organisation Capability Profile. The dimensions have synergistic effects, and they can only be unlocked if the organisation has capabilities across all dimensions. If the organisation has multiple dimensions with the same score, prioritise the dimensions listed under Organisational Readiness before moving to Ethics and Governance Readiness, Business Value Readiness, Data Readiness, then Infrastructure Readiness.
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