Studies suggest that up to four-fifths of companies are either using or exploring the use of AI in their business. However, nearly half of business executives believe that their utilisation of GenAI has contributed to an increase in the carbon footprint of their organisation, and only 12% see their company actively measuring the impact of AI’s integration into workflows. Many now feel that there is a critical need for a more informed approach to sustainable tech adoption. As we balance impact and opportunity of the AI paradox, it is time to have an honest conversation: “Is AI our sustainability saviour or saboteur?”
AI and sustainability
New solutions can often be seen as a panacea for common business hurdles. From increasing customer expectations to streamlining time-consuming workflows, companies can rush to embrace AI without fully considering the outcome or the impacts of its use.
There’s emerging evidence to suggest that data-driven insights can build a culture of environmental responsibility in every department. Business leaders can use this to ensure that their AI use feeds into both sustainable targets and wider corporate goals. Through utilisation of AI-automated analytics, companies can optimise energy use, reduce waste and enhance resource efficiency, leading to both financial benefits and lower greenhouse gas GHG emissions.
On the other hand, there are significant concerns that the pace of AI’s adoption will have a negative impact on the environment. The global demand for water resulting from AI is estimated to reach 4.2–6.6 billion cubic metres in 2027, and energy requirements are surging in response to AI’s demands. Without a structured and deliberate review of the implications, businesses that embrace it without visibility into the resulting GHG emissions or impact on natural resources risk undermining their ESG goals.
Navigating the AI-sustainability terrain
To drive lasting sustainable change, companies should ensure they have visibility into the net impact of their AI applications. In the past few years, we’ve seen AI adopted across industries to identify inefficiencies by locating patterns in data that indicate potential issues. This has allowed for proactive maintenance across a range of operational areas, including energy consumption, procurement and logistics. By merging this approach with analysis of environmental data trends, businesses can uncover cost savings and sustainability gains, such as reducing travel emissions, cutting waste and optimising resource use.
Engaging the supply chain
Once clear insights are visible, companies should mitigate AI’s environmental footprint by collaborating with their energy and data providers, optimising the use of renewable energy and implementing GHG reduction measures. While sustainability efforts should be in place regardless of AI usage, the alignment of AI-driven operations with green energy sources can help to offset the additional energy burden. For example, AI and machine learning models have the ability to improve supply chain forecasting, risk evaluation and compliance. Once able to process large datasets, forecast disruptions and log sustainability concerns, companies can proactively minimise emissions and decarbonise operations.
Accelerating critical assessments and discussions
For many organisations, AI is becoming a significant factor in their Double Materiality Assessments (DMA). DMAs help businesses to navigate the complexities of their operational risk and impact on the environment, and AI must be part of this review cycle. It’s key to shape AI governance, ethics and policies which align the technology’s use with sustainability goals. DMAs are the first step in developing clear strategies, robust safeguards, and actionable guidelines, where organisations can deploy AI responsibly – ensuring employees understand and adhere to best practices.
Proactive steps that companies could take include:
- Encouraging open conversations with all stakeholders around developing responsible AI solutions.
- Outlining clear guidelines for AI, ensuring accountability, human oversight and fairness.
- Constantly exploring new ways to optimise AI’s capabilities, minimising environmental impact while maximising societal benefits.
By structuring AI adoption around meaningful, efficiency-driven outcomes, such as automating repetitive tasks with greater accuracy, companies can reinvest the resulting benefits into more effective, sustainable and streamlined operations.
As AI continues to evolve, organisations utilising AI must engage in critical discussions about its implications from live debates to peer-to-peer experience exchanges. To steer the AI revolution, businesses should start with honest conversations about what trade-offs they are willing to tolerate between the promises of AI use cases and their progress towards net zero.
The path forward
Though AI consumes significant amounts of both energy and natural resources, businesses can reduce its impact through analysing trends and patterns in data, revealing opportunities for cost savings and next steps towards sustainability. To help in that process, companies should consider embracing sustainability reporting software that transforms complex data into actionable insights, driving both ESG strategies and regulatory compliance. By thoughtfully integrating AI within a structured ESG strategy, businesses can strike a balance—leveraging its benefits while minimising environmental impact.
David Picton
David Picton is Senior Vice President, ESG and Sustainability at EcoOnline Global.
