The development of AI is making great strides and intervenes in every aspect of our lives, from transport to work, the economy to agriculture, health to energy, as well as from climate change to inequalities. But recent technological developments allow us to look far beyond the benefits derived from the tools at our disposal in carrying out our everyday activities.
A wise use of the data and algorithms available nowadays actually offers the opportunity to implement systemic changes. Artificial intelligence can become the driving force for constructing a sustainable development model, the pillar of a new socio-economic standard based (also) on social and environmental parameters. It provides the launchpad for growth which is more than just economic, thanks to the use and sharing of data which would measure and restrict the environmental and social impacts of economic activities and reduce waste and negative external factors.
It is therefore important, first of all, to sweep away some of the benchmarks that we take for granted. One of these is the idea that only economic indicators can be the guiding light on the road to sustainable development. In other words, GDP, for example, can no longer be sufficient as an indicator of an economy's state of health. For the simple reason that the way in which it is calculated does not provide complete information about the well-being of the citizens of a state, inequalities or the sustainability of that given wealth.
The tools to support this change already exist and are available to local authorities and national governments, as well as to the European Union. In Italy, for example, Istat publishes every year the Bes report on Fair and sustainable well-being, which takes into account, in fact, a variety of factors when calculating the country's well-being. Istat uses a set of 130 indicators divided into 12 macro-areas, including "Environment" (which features protected areas, dispersion of the municipal water network, CO2 emissions, loss of biodiversity, etc.) and "Social relations" (therefore voluntary activities, social participation, non-profit organizations, civic and political participation).
Data sharing would enable more efficient sustainable development models to be built thanks to benefits of scale, which only potentially exist at the moment.
Italy has a dense network of small and medium-sized enterprises (SMEs) spread throughout the territory, from north to south. Being already somehow linked by sectoral networks and districts, AI can provide for them a highway to significant improvement, starting with the construction of a data governance model.
SMEs need, on the one hand, the data available to the public sector and, on the other hand, access to data sharing services designed specifically for companies – with indicators which include trust, transparency, neutrality, security – enhancing and making more efficient interventions in strategic sectors, from the environment to energy, agriculture to transport, and from manufacturing to public administration.
But even in a global context, the use and sharing of data, values and knowledge would allow us to build better, more valid and more efficient sustainable development models thanks to benefits of scale which only potentially exist at the moment. This also applies to the public sector. Many services need to become data-driven, accurate and accessible to all. In order to achieve this, at least at an early stage, it will be up to governments to channel their investments in this direction. This is the only way in which the research sector can multiply the potential for information currently available to us.
In this respect, the European Union seems to be offering a more than valid model. Funding from Brussels for AI research and innovation has risen to EUR 1.5 billion over the five-year period 2019-2024 (an increase of 70% on the previous period). But more needs to be done: the aim is to attract more than EUR 20 billion in total for investment in artificial intelligence every year for the next 10 years.
An AI-based model of sustainable development could also give new impetus to circular economy projects, which are essential if we really want to talk about environmental sustainability. Circularity makes it possible to gain the maximum from all the resources available and avoid unnecessary waste.
AI is already fully operational within many sectors, where it would only need to be enhanced. This is the case with agriculture, where smart systems make it possible to minimise water waste and to limit the use of insecticides and the effect of other pollutants. But not only this: greater use of advanced data and statistics would make it possible to calculate the amount of food consumed – and therefore to be produced – in each individual territory, thereby reducing stocks and waste.