How do I know if my company is ready to approach Artificial Intelligence? AI Singapore (AISG) has created an assessment model that companies can use to define their readiness on their own. It’s called AI Readiness Index (AIRI), and now we’ll see what it is and how to use it.
What is the AIRI assessment model?
Although there is a lot of talk about Artificial Intelligence, its possible applications are so vast as to make its evaluation complex. Managers need to understand how and where to start. Only then will its entry into the company be less traumatic and as productive as possible. It is necessary to check: that the technologies and skills required to use AI are in place; that one’s own organization is at a suitable technological stage to undertake the journey.
If you use a subjective approach to analyze these aspects, you risk making wrong considerations due to your personal experiences or those of your partners. Instead, a scientific approach based on objective data would avoid this risk.
Before analyzing the AI Readiness Index (AIRI) model, let me show you how I discovered this useful tool. I have been collaborating with the European Commission for almost two years as an external expert on the central evaluation committee. The project, in which I am involved, is called AI for Europe (AI4EU), and it is focused on developing a European Artificial Intelligence ecosystem.
Specifically, I deal with evaluating projects submitted by universities and European companies, thanks to my field experience. When I came across AI projects, I was looking for a tool that could allow companies to independently assess their level of readiness to adopt this powerful technology. Thus, I discovered the “Artificial Intelligence Readiness Index (AIRI).”
As part of the national AI program, AI Singapore (AISG) has developed a framework that, through well-defined steps, leads companies towards an independent assessment of their level of AI knowledge and usage. This model was supported by the National Research Foundation and hosted by the National University of Singapore.
How to assess whether we are ready to adopt AI in our company? By using the AI Readiness Index (AIRI) model, which allows a self-assessment of the level of knowledge and use of AI. Click To Tweet
If you are interested in delving deeper into my experience and perspective in the context of artificial intelligence and beyond, I recommend reading my book ‘Toward a Post-Digital Society: Where Digital Evolution Meets People’s Revolution’. This book provides a futuristic overview of how emerging technologies will influence businesses and the social structure as a whole.
How AIRI works
To evaluate the degree of readiness of a company, which would like to implement an Artificial Intelligence project, AIRI uses some parameters based on the success cases of companies with which AISG is in partnership. If the companies, who want to undergo the analysis, have already adopted AI, AIRI will evaluate the deviation between the current state and the desired one. In this way, organizations will obtain targeted programs that will allow the achievement of maximum efficiency compared to the starting situation. Abstract goals can then be materialized into actions that will accelerate AI adoption.
AIRI is based on four pillars and nine dimensions, as shown in the following infographic:
Specifically, the pillars that make up the model are interdependent. Their functioning is synergistic so as to show, in a holistic view, the current scenario of AI in the analyzed organization.
Organizational readiness: is your company ready to adopt AI?
In this phase, the organizational readiness of the company to adopt AI is assessed. The analysis considers human resources, their skills, and attitudes towards 4 dimensions:
- AI Literacy – assesses the degree to which human resources are educated about Artificial Intelligence, its applications, and whether they already use solutions that include AI.
- AI Talent – assesses whether there are resources in the organization capable of creating and managing Machine Learning models (i.e., models that contain the results of the training phase carried out through deep learning).
- AI Governance – assesses whether the company has defined strategic policies to guide the development and implementation of AI.
- Management Support – checks that management is willing to support AI initiatives by allocating the right human and financial resources for internal development.
The goal of this phase is to understand whether the organization has the necessary foundation to embark on the journey to AI implementation. At this point in the journey, the strategic role goes to people – their skills and readiness for innovation. Only then can the implementation of these new probabilistic algorithms move forward.
Business value readiness: what potential value will AI generate for your organization?
Business Use Cases are the only dimension present at this stage, and their evaluation poses a new question: have you established practical applications – real use cases?
If we know in advance the areas that will be affected by the innovation and the specific use cases, we will make the AI adoption process more efficient. For choice, we can get help from the value proposition. We will then know where AI generates the most efficiency.
An example? If we want to improve Customer Service, we can implement an intelligent virtual assistant. But also optimize production through predictive models for inventory or maintenance. Establishing these aspects in advance helps us define the implementation steps and make the overall process efficiency.
Data readiness: how the AI input data is organized
Data Quality and Reference Data are the dimensions of Data Readiness. In this phase, the organization’s level of readiness to handle the data that will serve as input to feed the algorithms is analyzed.
- Data Quality – does the organization have processes in place to ensure the completeness and accuracy of the data collected?
- Reference Data – is there a unique, standardized reference to the data?
As we allow digital technologies into the enterprise, we need to be very careful about the data we use to instruct them. While at the beginning of the use of AI, computer programmers minutely indicated the operations that software had to perform – in a deterministic mode – today, the training of algorithms is done through machine learning – a probabilistic mode typical of human beings. Therefore, training data are used (in jargon, we call them Training Dataset).
To proceed correctly with this evaluation, standardization of the data is essential. In this way, departments interpret the information in the same way and use the same entities, even if in different forms. Creating a shared repository is critical to achieving this standardization.
Structural readiness: the IT industry assessment
In infrastructure readiness, the question is: does your organization have the infrastructure in place to support the storage, retrieval of data, and training of artificial intelligence models?
To answer this question, you must initiate an assessment that crosses two dimensions:
- Data infrastructure – which includes: servers for processing, storage for archiving, organizational processes, and policies and guidance on how to manage data. The latter is also referred to as Data Governance as they formally manage data governance.
- Machine Learning infrastructure – which includes: human resources, organizational processes, and the tools needed to develop, train, and use machine learning models. Sometimes referred to as AI infrastructure or MLOps component, indicating a set of practices that aims to deploy and maintain machine learning models in production reliably and efficiently.
The final assessment: what is the score achieved?
Having reached this point in the AIRI model, a score will be obtained indicating the organization’s readiness for AI adoption. The result fits into the matrix shown in the infographic and allows us to access the final assessment:
The time to take this test is about 15 minutes, and you can do it independently by accessing the following link.
In addition, the approach with which to take the test should be to gain an educational experience from the course. In this regard, there are exciting indications on the explanatory page.
The result should not discourage you in any way. Still, it will serve to show what can be improved within the organization to implement technology as complex and fascinating as AI. Moreover, it will be enough to consult experts in the field to become AI Competent and take advantage of the benefits of what will become the real engine of our company.