Sustainability is now taking a front seat in core operations. As new legislation such as the UK’s Sustainability Reporting Standards may take effect this year, companies are now required not only to promise, but to prove their progress in initiatives related to environmental and social aspects.
But with this change comes a shared and complex problem: carbon tracking.
From product development and the supply chain, all the way through to end-of-life disposal and waste management, these new standards demand granular carbon data across all operations. But putting hard numbers to a business’ carbon footprint is not so easy. In fact, assessing and reporting carbon emissions is a relatively new practice for most companies, even large multinational companies. Value chains are complex, long, and not always transparent or traceable.
As businesses face growing pressure to demonstrate their sustainability efforts, the need to identify and implement clear, credible, and intelligent carbon tracking solutions is clear.
Where carbon tracking processes fall short
Many companies still rely on traditional carbon accounting – periodic manual reports, static averages, and self-reported supplier data. But this approach is slow, labour-intensive, and prone to errors and inaccuracies. Plus, the limited visibility over suppliers, transportation networks, and production processes only makes it more difficult for businesses to develop effective sustainability strategies.
Without this insight, businesses struggle to identify their major emission hotspots and miss opportunities for impactful reductions.
These emissions could stem anywhere from the value chain, such as procurements, business travels, or waste and the impact shouldn’t be underestimated. Recent data has shown that these Scope 3 emissions typically represent the majority of a business’s carbon footprint, often accounting for more than 70% total emissions. In the F&B sector, Scope 3 emissions can reach up to 95% of the total carbon footprint.
Companies can no longer afford to depend on static carbon reports that fall short in fast-moving supply chains. That’s where artificial intelligence (AI) and machine learning (ML) offer a promising solution, providing real time intelligence that drives tangible change.
Realising the potential of AI and ML
As is the case with many business operations, AI and ML are emerging as powerful tools for meeting critical demands and enhancing overall efficiency.
In terms of cutting down emissions, research assessing AI-driven decarbonisation scenarios across agriculture, transport, energy, and water, revealed that AI applications in these sectors alone could reduce worldwide greenhouse gas (GHG) emissions by 4% in 2030. That’s equivalent to the estimated annual emissions for Australia, Canada, and Japan combined that year.
But these AI/ML impacts aren’t just theoretical. This kind of transformation is already underway in resource-heavy verticals – take the food industry, for example.
Accounting for more than 34% of global greenhouse gas (GHG) emissions, food production has become a critical focus in the fight against climate change. Now, with specially trained ML models, food businesses can match their procurement data with a comprehensive database of emission factors, tracking carbon emissions as granular as the ingredient level. This real-time analysis provides precise and actionable data that helps guide their every decision, so they can track, report and make changes with complete confidence.
Advanced carbon tracking capabilities: Uncovering emission hotspots
With AI-powered carbon-tracking platforms, businesses can draw live signals straight from procurement, logistics, and production systems, replacing static, spreadsheet-bound estimates. Automating these processes minimises human error, and exposes previously invisible tier-2 and tier-3 suppliers responsible for a significant share of Scope 3 emissions. As such, businesses can pinpoint exactly where emissions accumulate along complex supply chains and take targeted action to unlock the transparency that consumers value.
Going beyond measurement, machine-learning models can also simulate “what-if” scenarios, predicting how simple changes might influence a company’s carbon balance. In the food industry, this could manifest in a recipe tweak, switching out carbon-intensive ingredients, or onboarding a new supplier. At the same time, AI-driven platforms can automatically format disclosures fit for regulatory frameworks, streamlining the otherwise time-consuming compliance process. Instead of a periodic scramble to gather and reconcile data, reporting becomes a natural by-product of day-to-day operations.
Stay one step ahead
AI and ML technologies are advancing more and more by the day. With sustainability tracking regulations becoming commonplace, executives need to go further than just regulatory box-ticking exercises. Current carbon tracking processes should be revisited and refined to not only streamline mandatory reporting, but to demonstrate real, ongoing change across the organisation.
Platforms with specialised AI and ML models offer the precision and real-time intelligence necessary to put proof behind their promise. These tools can surface hidden emissions that spreadsheets routinely miss, effortlessly maintain up-to-date carbon records, and strengthen net-zero commitments. As a result, businesses gain sharper operational insight that directly leads to lower costs and future-proofed growth.
