As the poster boy of all things new and good, generative Artificial Intelligence (Gen AI) has been talked about as the cornerstone that will power a company’s objective rapidly, with sustainable measures at the helm. As a foundation model that works on data fed to it, Gen AI is often considered the panacea for all the complex problems plaguing industries across the board. So, it is a big vote of confidence for the tech in context to nullify prominent problem statements of global temperature increase, rising water levels, and carbon footprint. All that remains to see is how well the tech fares.

At the recently concluded COP28, nations finalised pacts to focus on reducing emissions, thus aligning with the Paris Agreement goal of limiting global warming to 1.5 degrees Celsius above pre-industrial levels. While this task needs deep understanding of the factors contributing to the problem, we take hope from the titular tech (Foundation Model and Machine Learning) achievements in a relatively small timeline since its inception that we might have some semblance of a solution at considerably faster speeds.

With hopes and tech aligned, here are a few pointers on how Gen AI can play a constructive role in reducing carbon emissions.

GenAI can minimise considerable CO2 emissions through sustainable manufacturing processes run by machine learning algorithms. The platform can focus on efficient usage of natural resources and prioritise waste management for better end-to-end manufacturing process optimisation.

We see the green building concept being embraced by many organisations. Many of them are transforming their headquarters to run on more eco-friendly principles. Gen AI, with its learning mechanism, can forecast the amount of energy required. Such optimisation of grid management results in more accountability of resource consumption.

Any process needs constant analysis to make itself better. Gen AI, when working in collaboration with IoT technology-driven devices, can give out accurate carbon reporting values and insights into making the carbon-neutral process more efficient.

One of the key highlights of AI is its ability to study subject matter patterns. If allowed, Gen AI, with its thoroughbred approach, can analyse individual or collective data to suggest personalised actions for reducing carbon footprint, like adjusting energy consumption or travel habits. On an industry scale, we can also refer to it as understanding the critical pillars of scope emissions levels.

Every industry has planned downtime for maintenance purposes, but there are also curveballs in the shape of equipment failures. Gen AI, by going through the machine’s operational history, can help stakeholders access a timely service roadmap that not only extends the lifespan of the said equipment but bolsters their efficiency.

Management can funnel Gen AI’s immense capability for climate modeling and research by analysing vast datasets and simulating scenarios. This can aid key stakeholders to have a better understanding of the climate crisis and adopt strategies that directly mitigate carbon emissions.

Every process works on data. Data that translates into knowledge when shared with global players towards a singular objective. Gen AI platforms can empower knowledge sharing amongst industries to finetune their approach towards GHG emissions through potential partnerships in the form of joint research initiatives and technology transfer forums.

This article is authored by Lalit Das, founder and CEO, 3SC Solutions.