In recent years big brands and organisations within the FMCG industry have set about calculating and measuring their carbon footprints, and analysing where and how they can be reduced as they work towards net zero.
This has led to many organisations setting ambitious net-zero goals in the next few decades. L’Oreal for example has set aims for 95% of their ingredients to be bio-based and 100% of their packaging plastics to be either from recycled or bio-based sources by 2030, while Unilever made a recent commitment to halving the greenhouse gas impact of its products per consumer by 2030
But while many organisations are well underway in tackling their scope 1 and 2 emissions, some are ignoring a glaring blindspot. That is the carbon footprint embedded in the ingredients they use to produce the products they sell. And it’s here where the real source of carbon emissions can be found.
Commonly used ingredients, ranging from chemicals like acetone and isoprene to commodities like palm oil, often originate from fossil fuels or unsustainable, industrialised farming practices. These ingredients can be found in a diverse array of everyday products, like nail polish removers, paint strippers, detergents, soaps, processed foods, and cosmetics, as well as bike and car tyres.
Many brands are now actively seeking replacements to these key ingredients, but that’s proving harder than first thought. That’s because their chemical properties are so unique, so malleable and so effective, that they are proving hard to replace, and discovering new ingredients with the same properties is proving to be costly and time intensive. Until now.
Ingredients as usual is no longer enough
We’re sitting at the cusp of a biotechnology breakthrough, led by robotics and AI, which has the potential to reshape the ingredients industry for the better, but legacy processes used across the industry are holding this transition back.
Technologies such as artificial intelligence (AI) hold promise, but they are often applied ineffectively, with many companies scraping the data from their previous experiments in the hope that AI will somehow show them where they’re going wrong. The problem is that most experiments are run completely differently, with gaps in data, lack of context, and changes in testing approach. Chat-GPT is powerful because it has huge amounts of structured language data to train from. Unfortunately the same can’t be said for historical biological experiment data, which is anything but structured.
A shift is needed in the way the bioengineering industry operates, and it starts with placing AI at the core of its processes. This new process, we think, should consist of three core components – a bio tool that creates the base ingredients; robots that handle and run experiments; and AI that combines the information gathered from the bio tool and robots to optimise the process and suggest refinements for higher-quality outcomes.
Why climate needs AI
This new process would allow for thousands of experiments to occur simultaneously, generating structured data for AI models to learn from and guiding brands towards discovering more sustainable ingredients that work and have the same characteristics as the ones they’re seeking to replace.
By using AI, brands and businesses can wean themselves off carbon intensive ingredients to more sustainable options that feed into their net zero ambitions, and consumers can also buy in confidence, knowing that they’re not just getting the trusted quality they’re used to, but they’re also getting something that is sustainable too.
AI, we believe, shouldn’t be feared here, and its role as a force for good in the fight against climate change should not be underestimated. As consumer demand for sustainable options continues to rise, the integration of AI not only facilitates the discovery of sustainable alternatives but also accelerates processes, unlocking operational efficiencies and cost savings, too. A win-win solution for all.
