The improved capability comes down to advances in machine learning and cloud computing that combine with more powerful chips to make sense of the mountains of data produced by a growing army of connected devices.
But it is not only about machines. Consumers are increasingly sensitive to brands that can demonstrate corporate social responsibility (CSR) in tangible, believable ways.
A large majority (71%) of people surveyed in the US think businesses should take a stance on social movements and 75% are more likely to shop at companies supporting issues they agree with, according to a study by Clutch.
Measuring ESG criteria
Evidence that a company is mitigating its exposure to ESG risks and ensuring long-term sustainability is a strong metric for impact-aware investors.
Investment managers are feeling the pressure to measure ESG criteria in their portfolios, but poor data and unreliable tools for comparing long-term risk make this a labour-intensive, costly exercise.
This is where AI comes in. Its ability to learn and adapt lends well to greater automation of complex analytical tasks.
For starters machine-learning technologies can filter out the junk data, leaving a richer set of ‘essential data’ that investors can focus on for what S&P Global calls “sustainable investing at scale”.
The market intelligence company reports that smart algorithms have already been trained to analyse the tone or sentiment of content published by companies to better understand their green credentials or commitment to social equality, for instance.
“If ESG investing involves considering the material opportunities and risks of sustainable decision-making, AI provides both tremendous benefits and risks to watch for,” notes S&P Global, adding that AI can itself be considered an ESG risk for companies.
How a brand stacks up in the ESG ‘goodwill calculation’ clearly affects buying decisions and has a proven impact on profitability. That makes it a major concern for investors, too.