But before we get into the nuances of different capacity-building strategies, let's first discuss some commonalities. A comprehensive capacity-building framework should take into account the different roles that members of society will play in an AI ecosystem. These can be broadly categorized into ‘technical’ and ‘non-technical’ roles. Technical roles include anyone who will be involved in the design, development, operation, or maintenance of an AI system. These can include roles such as Data Scientists, Machine Learning Engineers, Data Architects, MLOps Engineers, and many others. Each of these roles has its own strategy and set of curricula that can be designed at different levels to prepare them for the job market.

While it may seem like a challenging enough task to design programs for technical roles, it is actually the non-technical ones that pose the greatest challenge. Non-technical roles start with generic notions like increasing public awareness of AI. This by itself is a significant challenge as it requires the presence of a basic level of technology literacy among the public - and this is where we start getting into nuances across societies. While a developed country like Finland can easily develop a university-level AI course and use it to educate 1% of the population (Finland being a country with a 100% literacy rate and where roughly 75% of the population hold a college degree), a developing country might first need to ensure that the majority of its population can read and write before introducing them to something as sophisticated as AI.

The picture is not necessarily that grim, however, as evidence has shown that people, even those without basic access to education, are still able to absorb knowledge such as the use of smartphones, if a) they can see the value in it, and b) if that knowledge is presented to them in a manner which suits their needs.
So, going back to the Finland example, a ‘general awareness’ course would simply need to be a text-based, online course in English. While in a developing country, the same course might need to be delivered as a series of short videos on television by the country's main broadcaster, in a colloquial language people will understand. Moreover, it needs to be full of demonstrations of how to perform certain tasks and possibly supported by a phone hotline to answer people's questions.

It is up to each country to decide which level of general awareness suits its needs. However, it is equally important for global leaders to be aware of those differences in needs and starting points, and to not assume that it is equally easy for countries to implement AI awareness programs. Developing nations need all the support they can get, not in the least because their populations are often the most vulnerable to abuse.

General awareness programs should cover the basics of AI, its benefits and limitations, and crucially, its risks and ethical aspects. If the country has any legislation regarding AI, data protection, or similar, this should also be included. Most importantly, such programs should dispel the myths around AI, which are unfortunately still prevalent, especially in the developing world, such as its impact on the economy, or even worse, potential superintelligence that will make humanity extinct.

Apart from general awareness programs, a vital component of a society's readiness for the age of AI is to train its ‘domain experts’. The domain expert is the non-technical professional who ‘owns’ the problem to be solved by AI. Examples include healthcare professionals, agriculture specialists, marketing professionals, legal experts, and many many others depending on the domain of application of AI. In my view, no AI project can be successful without the involvement of well-trained domain experts. These are the individuals who will advise on the exact problem to be solved, data availability, challenges, validity and relevance of results, and many other aspects so crucial to a project's success. Training domain experts is best done by introducing a compulsory ‘Introduction to AI and Data Science’ course into universities, or even secondary schools if possible. This should be tailored to each domain, for example, ‘AI in Agriculture’, ‘AI in Healthcare’, etc, and a version of it should be developed for professionals currently working in that domain. This will ensure a roster of experts able to collaborate with technical teams, and even able to identify and suggest AI projects by themselves.

The last, important group of non-technical roles are leaders, including those in government, business, civil society, or any other domain. While they may not need to develop the deep expertise of a domain expert, they definitely need more preparation than a general awareness course. They need to be taught how to think about AI as an investment: how to identify an AI project, how to allocate the right investment, find or train the right people, assess its success, and, if relevant, develop their organization's own AI and Data strategies. This group also includes current and aspiring entrepreneurs, who need to assess their readiness to become producers or users/consumers of AI.

The above is by no means a comprehensive capacity-building strategy for AI, but rather some thoughts on how societies can start thinking about developing such strategies to suit their needs and priorities. International cooperation and knowledge sharing are vital enablers to the success of AI adoption worldwide.