Although AI can enhance ESG performance and help to identify risks, it can also create significant environmental impacts and present ethical, legal and compliance challenges.
This means that developing a robust environmental, social and governance (ESG) strategy is more important than ever.
As new frameworks such as the EU AI Act and ISO/IEC 42001:2023 come into play, organisations must ensure that AI supports their wider ESG objectives.
In the following guide, leading certification body ISOQAR offers practical ways to integrate responsible AI within ESG commitments that support innovation while driving ongoing compliance, ethics and trust.
Understand what AI means for your organisation
From email management to business intelligence, AI is not a single technology but a spectrum — whether it is machine learning (ML), generative models or large language models (LLMs).
Each of these requires separate resources and will impact your ESG strategy differently.
Not all organisations interact with AI in the same way and businesses generally fall into two categories: AI users and AI developers.
- AI users primarily consume AI tools for analytics, marketing or customer service. Here, ESG considerations often focus on the ethical and responsible use of AI systems.
- AI developers: These organisations build, train and deploy AI models. Their ESG responsibilities are more extensive and often cover the full lifecycle of AI development.
Conduct an internal audit
Data centres (used to run AI models) consumed around 415 TWh of electricity in 2024, roughly 1.5% of global electricity use. And with AI workloads expected to double that demand by 2030, understanding the environmental footprint of your AI systems and defining how AI will fit into your environmental goals is critical.
A good starting point is to conduct an internal audit of current and planned AI systems within the organisation. Some key areas to assess are
- environmental (energy efficiency, carbon footprint, energy sourcing and hardware lifecycle impact)
- social (data privacy, inclusivity, user safety, transparency and fairness)
- governance (human oversight, accountability, compliance, cybersecurity, system reliability, performance monitoring).
By thinking of ESG integration at the design and development stage, you can prevent ethical and sustainability risks from becoming compliance issues later.
Establish ethical AI governance
Governance, often overlooked, is one of the key drivers of ESG targets. And within the governance pillar, responsible AI use starts with accountability.
Despite heavy investment, 74% of companies say they’ve yet to see real value from AI, often because governance and ethical oversight lag behind technological ambition.
This means that, from the outset, organisations should set clear policies defining acceptable AI use, accountability structures, and human oversight requirements.
Organisations will also want to establish a responsible AI framework to work towards. For AI developers, this could look like embedding ethical considerations throughout the model lifecycle, from data sourcing and training to validation and deployment.
And each organisation will want to create a responsible AI framework that addresses fairness, transparency and privacy, ensuring that ethical principles are fully operational.
Overall, it is key to create an internal culture of responsibility and transparency throughout the entire organisation, making AI a tool for achieving long-term ESG objectives, rather than a potential liability later down the road.
Operationalise AI within ESG strategy
AI is already proving its potential; according to research, more than half of global companies report that AI has had a major impact on their decarbonisation and sustainability planning.
Although governance is key in responsible AI use, it must also translate into measurable outcomes.
This is achievable by operationalising AI carefully, and establishing ESG-aligned performance metrics such as energy efficiency, fairness scores, bias detection rates and compliance indicators.
Any outdated ESG reporting frameworks should be updated to include AI-specific risks and outcomes ... and cross-functional training should be provided to teams and stakeholders to ensure that factors such as ethical AI practices, risk management and data security are all understood.
Prepare for certification to drive accountability
In a world in which compliance is everything, certification demonstrates that responsible AI is more than just a marketing statement but is embedded into the core of the organisation.
ISO/IEC 42001 guides organisations through key steps to responsible AI use, such as establishing an AI Management System (AIMS), conducting internal audits and refining processes.
Research shows that 73% of UK executives say that a lack of clear AI governance frameworks is their biggest barrier to scaling responsibly, and that 63% of senior leaders say that they would trust AI tools more if they were validated by an external organisation.
Achieving certification not only improves stakeholder trust, but it supports compliance with global regulations and demonstrates that your AI systems are ethical, transparent and aligned with ESG principles, both now and for the future.
Implement rigorous testing and monitoring
Responsible AI is an ongoing process. To ensure that responsible AI use continues, rigorous testing and monitoring are needed on a consistent and ongoing basis.
Continuous testing and monitoring should be built into governance to track model behaviour, identify bias and ensure systems evolve responsibly as data and regulations change.
This will not only enhance security and reliability but also enable innovation within safe boundaries, giving your organisation a competitive advantage — all while demonstrating leadership in ethical and responsible AI deployment.
AI can fully complement ESG strategies if managed responsibly.
By adopting strong frameworks and setting clear and attainable targets, organisations can integrate ethical and sustainable AI into decision making, reporting and innovation pipelines.
In doing so, AI moves from being a compliance challenge into a driver for ethical innovation, sustainable growth and lasting stakeholder trust.