Narratives around the power of AI to revolutionise our work on ESG often revolve around bold claims – that automated reporting can happen essentially overnight and machines will do all the work. But the reality is much more grounded: yes, AI can drastically improve and accelerate our ESG programs but true impact comes from practical, well-designed machine learning systems that solve real problems.
This is the essence of what we might call “targeted AI”: the intentional, efficient use of machine learning to solve practical problems without defaulting to large, energy-intensive systems. How AI is used matters as much as what it can do, and in sustainability this is particularly the case
Sustainability is a data challenge
“You can’t manage what you don’t measure” has long been true in finance; it’s also true in sustainability. Environmental performance has become a data problem – one that demands the same level of precision, structure and systems thinking as financial accounting.
Companies today are under pressure to make measurable changes to their environmental and social impacts. Yet too many corporate sustainability efforts remain fragmented, symbolic or simply disconnected from core business operations.
Frameworks like the CSRD in Europe and UK’s SDR now require investor-grade, auditable emissions data across Scopes 1-3, demanding real interoperability, data granularity and a shift to continuous, system-led monitoring. Legacy IT tools and siloed reporting processes can’t adequately meet the demands of today’s regulations.
Carbon is the new cost centre. Managing it well will require the same transformation we brought to enterprise finance decades ago, only faster. The good news is that we’ve achieved this kind of overhaul before. The challenge? This time, organisations don’t have 20 years to get the job done – realistically, they have five.
Scale without the strain
While AI is often portrayed as a fix-all for any time-consuming task, its real power lies not in cutting corners but in targeted, intelligent use. The most impactful AI applications in ESG are often narrow in scope but deep in impact: automating data collection from disparate sources and formats, cleaning complex and de-centralised data sets, and ultimately eliminating manual error-prone processes that hamper progress.
Any company knows reporting deadlines can quickly become overwhelming when massive data volumes are handled manually. Teams often spend weeks wrangling spreadsheets, reconciling inconsistent metrics and trying to make sense of siloed systems that don’t talk to each other.
Companies seeking to implement AI solutions in an efficient, targeted way should consider some of the most arduous tasks:
- Consolidating data from multiple sources/departments in the business
- Spotting and flagging data anomalies
- Mapping different data structures into a unified format
The likelihood is that these are the tasks in which AI can make the most material difference – speeding up reporting cycles, improving accuracy, and allowing human teams to focus on high impact tasks such as analysis and strategy, instead of data cleanup. All of this moves sustainability teams faster from the data collection and analysis phases, to taking data-based action. This is where they create true business value from the data. To name just a few examples: identifying cost or process efficiencies, highlighting risks to be avoided or mitigated, spotting new product or market opportunities, or being able to evidence ESG credentials to investors and other stakeholders.
This is where targeted AI delivers real value. Rather than trying to replace human intelligence, it supports it – automating high-volume, low-value tasks. Crucially, this kind of AI doesn’t require a full systems overhaul; it can be layered into existing workflows, offering immediate efficiency gains without adding complexity. In a reporting landscape where speed, accuracy, and transparency are non-negotiable, this kind of support is no longer a luxury – it’s a necessity.
However, AI is not a silver bullet. It can’t (and shouldn’t) replace ethical decision-making, goal-setting, or stakeholder engagement. It also struggles when asked to interpret ambiguous context, set priorities, or make tradeoffs based on corporate values.
Rather, AI thrives in structure. To be impactful it needs a human to work alongside it. Let AI handle the plumbing: data, formatting, compliance, so that people can focus on what matters: making smart, sustainable decisions. It’s not just good practice, it’s good business.
Efficiency gains vs energy drains
Of course, AI’s benefits aren’t without tradeoffs. Large language models are energy-intensive, and the infrastructure supporting them is increasingly responsible for significant greenhouse gas emissions. Google, for example, recently reported a 51% increase in carbon emissions since 2019, largely attributed to the rising energy demands of AI workloads and data centres.
So while AI can accelerate sustainability efforts, its own environmental footprint must be part of the equation. This is why intention matters. Targeted AI prioritises computational efficiency, reports on its own impact, and focuses on doing more with less.
Move early and intelligently
As ESG reporting requirements continue to evolve, it’s vital that businesses stay ahead of the curve. The low-carbon economy is coming, and businesses that want the upper hand against competitors can’t afford to be weighed down by slow, siloed, error-prone reporting processes. Reducing carbon emissions takes time and effort. Businesses that act now will position themselves to lead; those that wait risk playing catch-up in five years’ time when the UK approaches its 2030 climate target.
Beyond compliance, the use of targeted AI in ESG reporting enables companies to get a full picture of their decarbonization progress faster – and act on it. By embracing practical, well-engineered tools, companies can not only meet regulatory demands but unlock real strategic value. In the race to build a more sustainable future, those who move efficiently and intelligently, empowered by AI, will lead the way.
