According to the latest IPCC report, less than 1% of the objective to decarbonise the world economy by 2050 has been achieved. In the face of this urgency, an increase in environmental constraints on companies seems unavoidable. Companies must act now and not wait for regulation, which will most likely result in accelerated and costly decarbonisation of their production processes.
To reduce their greenhouse gas emissions, companies must take into account emissions across the entire value chain, including the parts they do not directly control, known as Scope 3 in the context of the Greenhouse Gas Protocol. CO2 emissions must now be included in the parameters for optimising operational efficiency. To enable companies to make informed decisions in a complex and environment with many variables, the use of artificial intelligence is crucial. Using self-learning algorithms helps to reduce emissions in supply chains by accurately anticipating supply and demand, optimising transport routes and storage and identifying the least carbon-intensive products.
Companies wishing to make AI a prominent part of their decarbonisation programme must remove all obstacles to deploying it effectively. These include legacy siloed data governance and a legacy applications base that is not very open to the ecosystem of external stakeholders, as well as the difficulty of accessing in-demand AI skills (programming, Big Data, machine learning and modelling, robotics, etc.) and a lack of C-level attention to AI-leveraged decarbonisation. The benefits of an AI-based approach to reducing CO2 have the potential to be very significant. However, as with every technology-leveraged project, companies should identify its positive impact in relation to carbon emissions prior to launch.
