AIRI Assessment Result: Your Organisation is AI Aware
Organisations that fall within the same AIRI category tend to exhibit similar capabilities and characteristics; the table below illustrates the common capabilities, characteristics, and AI adoption suitability for organisations in each category of AI readiness.
| 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 | |
Interpretation of AIRI Result
It is a common misconception that AI adoption is only suitable for larger or technology-based organisations. On the contrary, 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. 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 organisations could identify better AI use cases, procure relevant AI solutions, and potentially benefit more from AI adoption. AI Ready organisations 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 organisations 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 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. 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.
Your Organisational Profile and Other Companies in the Manufacturing Industry
The chart on the left shows your responses and their corresponding score based on AIRI. The score ranges from 0 to 1; a higher score indicates better capability in the respective dimension. The chart on the right shows the average AIRI result of other companies in the same industry as your organisation.
Note: 0 – AI Unaware, 0.33 – AI Aware, 0.66 – AI Ready, 1 – AI Competent
Your Organisational Profile and Other MNC Type Companies
The chart on the left shows your responses and their corresponding score based on AIRI. The score ranges from 0 to 1; a higher score indicates better capability in the respective dimension. The chart on the right shows the average AIRI result of other companies with the same organisation type as your organisation.
Note: 0 – AI Unaware, 0.33 – AI Aware, 0.66 – AI Ready, 1 – AI Competent
Interpretation of Organisational Profile
The organisational capability profile serves as a guide for an organisation looking to improve its AI capabilities. An organisation should seek a balanced profile by having all dimensions with the same score based on their desired capability; otherwise, the weakest dimension in a lopsided profile might hinder the organisation from achieving its AI ambition and reaping the synergistic effects across dimensions. For instance, an organisation that is strong in AI Talent but weak in Data and ML Infrastructure cannot develop AI models effectively. This is because their AI talents lack the necessary infrastructure to perform their job.
It is rare for an organisation to have an overly lopsided profile. For example, an organisation without strong Management Support is unlikely to have the required resources to invest in other dimensions. Similarly, an organisation with weak AI Literacy is doubtful to identify suitable business use cases, appreciate the importance of Data Quality, and understand the necessity for AI Ethics and Governance. An organisation with an overly lopsided profile might want to review the self-assessment responses to ensure their accuracy. By identifying the weakest dimensions, the organisational capability profile enables an organisation to focus on dimensions that could significantly improve its overall AI capabilities.
Approach to Improving AI Readiness
Below is a suggested approach for organisations to benefit from AIRI assessment and improve their AI readiness:
1. 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 offering (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 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 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.
2. 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 at AI Ready. Finally, organisations looking to develop their customised AI solution should be at AI Competent level.
3. 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.
AIRI Suggestion
The AIRI Suggestion is designed to provide your organisation with a more detailed understanding of how you can enhance your AI readiness, with actionable steps that you can take to achieve your goals. The fundamental premise of the AIRI Suggestion is that each organisation is at a specific stage of AI readiness, with distinct requirements and challenges to overcome. For example, if your organisation is currently moving from AI Unaware to AI Aware, you may be embarking on your first AI project using an off-the-shelf solution. Conversely, if you are progressing from AI Aware to AI Ready, you may be seeking to develop your first customised AI solution. Finally, if you are moving from AI Ready to AI Competent, you may be focused on developing multiple AI solutions using your in-house team. The AIRI Suggestion serves as a guide for organisations that are committed to enhancing their AI readiness, while acknowledging that different organisations may opt to stay within a specific AI readiness category that aligns with their business needs. To help organisations advance to the next level of AI readiness, the AIRI Suggestion offers a range of recommendations that may be relevant for your organisation to consider and implement as you work towards achieving your goals.
| Pillar | Dimensions | AI Unaware --> AI Aware (Acquire AI Solution - e.g. customer service chatbot) |
AI Aware --> AI Ready (Build a bespoke AI solution) |
AI Ready --> AI Competent (Build many AI solutions with an internal team) |
AI Competent |
| organisational Readiness | Management Support | Set a budget and allocate resources for the first AI project, as well as to enhance employees' AI literacy | Establish a project team comprising of individuals who possess a fundamental understanding of AI (e.g. AI champions) to identify the AI solution requirements and search for a suitable provider | Establish company's AI strategy and allocate budget and resources | - |
| AI Literacy | Encourage all employees (including non-engineers, sales, marketing, HR) to attend non-technical AI classes such as AI For Everyone (AI4E) webinar, whether online or offline | - | |||
| AI Talent | Identify tech-savvy employees who can be AI champions and empower them to experiment with and adopt off-the-shelf AI solutions. | Enhance the skills of a project team to define the scope of the AI project | Hire and build an AI engineering team consisting of AI engineers, MLOps engineers, product managers, data curators, AI researchers | - | |
| Employee Acceptance of AI | Encourage AI champions to organise training sessions for employees on how to effectively use the tested AI solutions. | - | |||
| Experimentation Culture | Empower AI champions to work with employees to explore AI-enabled solutions | - | |||
| Business Value Readiness | Business Use Case | Choose one business process where AI solutions could be implemented | Ideate and prioritise the potential AI use cases that have the highest business value potential | - | |
| Ethics and Governance Readiness | AI Governance | Gain familiarity with AI governance standards or frameworks relevant to the AI solution | Implement basic AI governance guidelines | Create policies and procedures to ensure that the implemented AI solutions are functioning appropriately, such as ensuring that AI models are trained on unbiased data, and monitoring the performance of AI models | - |
| AI Risk Control | Identify potential risk of AI solution | Implement basic AI risk control guidelines | Appoint a leader (e.g. Chief Data Officer, Chief AI Officer) to standardise AI risk controls across the organisation and to conduct rigorous testing on their existing AI solutions. | - | |
| Data Readiness | Data Quality | Put in place processes for collecting and cleaning the data. Educate all stakeholders on importance of quality data | Assign employees with formal responsibilities to manage datasets that have potential AI use cases | Appoint a leader (e.g. Chief Data Officer, Chief AI Officer) to manage data management processes | - |
| Reference Data | Put in place a common vocabulary (e.g. a set of standardised codes or labels) for data use within the organisation to ensure that data is accurate and consistent | Consolidate the datasets into a unified database at a sub-organisational level and structure the data in a standardised format | Consolidate the datasets into a unified database at an organisational level and establish and upkeep the data definition, catalog and metadata | - | |
| Infrastructure Readiness | Machine Learning (ML) Infrastructure | Set aside budget for infrastructure (i.e. cloud or on-premise) suitable for AI solution | Allocate a budget for cloud or on-premise servers to support the low-code platform or turn-key solutions and access the deployed AI solution remotely. | Allocate a substantial, dedicated budget for deploying a distributed system. Implement MLOps, leverage APIs, develop AI-enabled software, and integrate AI into most projects. Include AI as a component of the product offering | - |
| Data Infrastructure | Organise the tabular datasets in Excel files and non-tabular data (e.g. image, text) in separate folders for efficient data storage and management | Implement a suitable database technology (e.g. relational database, object-oriented database, graph database) for supporting the AI project | Implement a wide range of suitable database technologies (e.g. relational database, object-oriented database, graph database for supporting multiple AI projects | - | |
| Note: AI unaware organisations are assumed to have some level of digital readiness (e.g. converted analog data to digital data) | |||||
How Can AI Singapore Help?
AISG has programmes to help organisations to improve each dimension of their AI readiness. Table (below) shows the mapping of AISG’s programmes to each category of AI readiness; organisations can refer to the table to understand what programmes are suitable for them based on their existing AI capabilities. For instance, an AI Unaware organisation could consider asking their employees to watch AI for Everyone (AI4E)®, followed by attending AI Clinic to learn use cases relevant to their industry or function. Organisations could also engage external training providers or their trade associations to improve specific dimensions identfied under AIRI. AISG has signed MoUs with multiple trade associations and Higher Learning Institutes to promote AI awareness, adoptions, and applications. Interested parties could check with AISG or their respective trade associations on existing collaboration arrangements. Again, not every organisation needs to reach AI Competent. The ideal AI readiness is dependent on the organisational business 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. Along with blockchain, cloud computing, and data analytics, AI is the ‘ABCD’ of industry 4.0. Early adopters of AI will have sustained competitive advantages as they established essential ML infrastructure and processes of data collection and data quality control. Furthermore, through capabilities building and experience, the management and workforce will be well-versed in AI to identify AI use cases, use AI solutions, or develop AI products. These capabilities cannot be easily replicated and require significant time and effort to reap results. The best time to start was yesterday; the next best time is today.
| AI Unaware | AI Aware | AI Ready | AI Competent | |
|---|---|---|---|---|
| Strategy to improve AI readiness | Increase AI literacy of organisation | Prepare organisation to adopt AI solution | Help organisation to adopt AI solution | Deepen organisational AI capabilities |
| Organisational Readiness | AI For Everyone (AI4E)® | |||
| AI for Industry (AI4I)® | (AI4I)® (Advanced) | AI Certification | ||
| AI Ready Clinic | ||||
| Business Value Readiness | AI Discovery | |||
| Data Readiness and Infrastructure Readiness | 100E Experiments (100E) + AI Apprenticeship Programme (AIAP)® | |||
| AIAP-X | ||||
Mapping of AISG programmes to each AIRI classification
Thank you for undergoing the AIRI assessment.
Please get in touch with us if you have questions about AIRI, would like to know more about our programmes, or require further advice on improving your organisation’s Al maturity.
