AI Maturity Index (AIMIND)
AIMIND is an industry-focused AI readiness assessment framework developed by the SME Committee under GPAI’s Innovation and Commercialisation working group. It crystallizes and distills the critical success factors for AI adoption based on the combined industry experience of committee of experts across industries and countries.
| 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 | |
AIMIND Components
AIMIND consists of 5 pillars, which map to 12 dimensions. The 5 pillars are interdependent and synergistic.
Organizations with strong Organizational 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 5 main pillars of AIMIND provide a holistic assessment of an organization’s readiness to adopt AI.
The five pillars and twelve dimensions assess a specific area that contributes to the overall AI readiness of organizations. In each pillar, it has several dimensions and each dimension is assessed at four levels of AI Readiness:
- AI Unware
- AI Aware
- AI Ready
- AI Competent
Organizations can exhibit different levels of AI Readiness across the dimensions.
| Pillars | Dimensions | Assessments |
|---|---|---|
| Organizational Readiness | Management Support | Whether the organization 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 organization 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 organization has an experimentation culture for employees to explore and develop AI use cases | |
| Ethics and Governance Readiness | AI Governance | Whether the organization has appropriate governance to avoid unintentionally harming end-users |
| AI Risk Control | Whether the organization has a proper classification of the risk level of AI systems | |
| Business Value Readiness | Business Use Case | Whether the organization has identified suitable AI use cases and assessed their value propositions |
| Data Readiness | Data Quality | Whether the organization 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 organization has appropriate and sufficient ML infrastructure (e.g., GPU, memory) to support AI model training and deployment |
| Data Infrastructure | Whether the organization is using appropriate data infrastructure (e.g., data lake) as a central repository of data |
AIMIND Insights
It is a common misconception that AI adoption is only suitable for larger or technology-based organizations. On the contrary, AI Unaware and AI Aware organizations, even if they lack data, talent, or ML infrastructure, could adopt ready-made AI solutions for their core or peripheral business activities. For instance, an AI Unaware or AI Aware law firm could implement a chatbot on its website to help answer queries from clients. The critical difference is that AI Aware organizations could identify better AI use cases, procure relevant AI solutions, and potentially benefit more from AI adoption. AI Ready organizations typically can integrate AI features into existing products via Application Programming Interface (API). For instance, an AI Ready consumer insight firm could make API calls to AI services provided by cloud service providers to analyse customers’ sentiments. AI Ready organizations could also readily explore open-source and pre-trained AI models to infuse their products or services with AI features, thereby enhancing their competitiveness. AI Competent organizations 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. Organizations should assess if their current AI capabilities support their organizational objectives. If there is a mismatch, organizations could refer to their organizational capabilities’ profiles as a high-level guide on specific areas to target and improve. An important point to note is that not every organization needs to reach AI Competent. The ideal AI readiness is dependent on the organizational objectives. Nonetheless, given the pervasiveness of AI technology, organizations 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.
Approach to Improving AI Readiness
Below is a suggested approach for organizations to benefit from AIMIND assessment and improve their AI readiness:
- Determine whether current AI capability supports organizational goals
AI, similar to other technologies, is a tool that could help organizations increase their competitiveness via higher automation (cost-saving), better product offering (revenue), or deeper analytics capabilities (insights). Organizational goals serve as the north star for technology adoption; adopting AI without a clear direction and purpose will bring disappointing results. Therefore, organizations should work backwards from their organizational 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, organizations could decide whether the use cases justify hiring a team of AI Engineers to develop customised solutions. Not every use case require customised solutions; organizations, especially AI Unaware and AI Aware, should first look at commercially available solution before developing 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 organization gain experience using AI applications. Organizations should also consider whether having the 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 organization needs to be at
Organizations could refer to the Interpretation of AIMIND results to understand which AI capabilities they need to be at. Generally speaking, organizations looking to adopt commercially available solutions could be AI Unaware or AI Aware. Organizations looking to integrate AI features, such as AI services from cloud providers, into their products should be at AI Ready. Finally, organizations looking to develop their customised AI solution should be at AI Competent level. - Focus on the weakest dimension first
Organizations looking to improve their AI readiness should focus first on their weakest dimension based on their Organization Capability Profile. The dimensions have synergistic effects, and they can only be unlocked if the organization has capabilities across all dimensions. If the organization has multiple dimensions with the same score, prioritise the dimensions listed under Organizational Readiness before moving to Ethics and Governance Readiness, Business Value Readiness, Data Readiness, then Infrastructure Readiness.
AIMIND by AI Singapore and Initiative for Applied Artificial Intelligence 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.)