Artificial Intelligence and Sustainability-Oriented Innovation

05/08/2020 40

Throughout the history of management, various authors have contributed conceptual models so that companies can incorporate sustainability as a strategic area. For example, the triple bottom line is a concept coined in the mid-1980s by authors such as Freer Spreckley, which gained great notoriety with the 1998 publication of Cannibals with forks: the triple bottom line of 21st century business by John Elkington.

The triple bottom line is an accounting framework that promotes sustainable business based on three fundamental dimensions: economic, social and environment.

The adoption of the triple bottom line provides significant benefits for the company, since it facilitates access to new potential markets, increases employee motivation, encourages innovation, improves reputation and loyalty to customers.

Several studies, such as that of Richard Adams et al. (2012) Entitled “Innovating for Sustainability, A Systemic Review of the Body of Knowledge”, provided fundamental keys and facilitated the development of a conceptual model designed to help position different types of activities in the area of ​​ Sustainability-Oriented Innovation.

The Canadian Network for Business Sustainability (NBS) published in 2012 the executive guide entitled “Innovating for sustainability”. The guide includes a conceptual framework to develop sustainable businesses.

The first level is named “Operational Optimization”. The objective of innovation at this level is characterized by compliance and efficiency. What is sought here is “to do the same things better”.

The second level is named “Organizational transformation” and the objective of innovation is to develop novel products, services and business models. In this case, what is sought is “doing good by doing new things”.

The third level is “Systems Building” and is characterized by the development of new products, services or business models in collaboration with other stakeholders. This last level allows achieving business returns taking into account the triple income statement.

In this sense, AI can be a great ally for the development of Sustainability-Oriented Innovation. Remember that AI can be defined as the science and engineering that enables the development of machines and computer programs capable of solving problems that normally require human intelligence.

In this month’s article by Bernard Marr for Forbes titled “10 Wonderful Examples Of Using Artificial Intelligence (AI) For Good,” the author reviews 10 examples of using AI to solve social and environmental problems. AI allows us to analyse social problems from a different perspective.

The 10 examples are: cancer detection, bee protection, tools for people with disabilities, climate change, wildlife conservation, fighting world hunger, reducing inequality and poverty, detecting false news, evaluating medical images and prioritizing updates.

The case of climate change is especially interesting since, according to the study “Global Risks Report 2020” by the World Economic Forum and Marsh & McLennan, concerns about environmental risks have increased in the last decade.

For the future, the main threats are: failure in the action of climate, extreme temperatures, loss of biodiversity, natural disasters and damage caused to the environment due to human action.

Bernard Marr explained in his article for Forbes how AI could be fundamental for solving the problem of climate change. Machine learning algorithms power approximately 30 climate models used by the Intergovernmental Panel on Climate Change.

Artificial Intelligence can also help educate and predict the impact of climate change in different regions. For example, researchers from the Montreal Institute of Learning Algorithms (MILA) use GAN (generative adverse networks) to simulate severe storm damage and sea level rise.