AI is not a project. It is not an IT initiative. Nor is it a tool that sustainability functions can choose to consider later. Used well, AI is an opportunity to bring sustainability closer to the decisions where it can make the greatest difference to the business.
AI is changing the way knowledge work is done. Since much of the sustainability function’s work is built around analysis, reporting, communication, risk assessment and decision support, it will be affected at its core. The question is therefore not whether AI will change sustainability work. The question is how you, as a sustainability leader, choose to lead that change.
Sustainability has been changing for a long time. Each shift has moved the focus, changed ways of working and raised expectations of both the sustainability leader and the function as a whole. In many organisations, sustainability work has historically been driven by a specialist function, often focused on policies, codes of conduct and reporting. Success has been measured by how well the organisation has met external requirements and been able to explain and account for its work.
The head of sustainability and other members of the function have primarily acted as experts, and the knowledge most highly valued has included expertise in regulation, reporting, environmental and sustainability matters, and internal control.
After several years in which CSRD and other regulations have required considerable effort, the playing field has shifted again. Sustainability has not become less relevant, but the questions are increasingly framed in terms of competitiveness, resilience and business value. This means that sustainability work can no longer stop at reporting and compliance. To drive development forward, sustainability needs to be integrated into strategy and the business. As that happens, internal boundaries shift, because sustainability is no longer a separate function alongside the business, but part of the business itself.
Areas such as investments, risk management, innovation, procurement, product development and finance are already being affected by sustainability issues. When sustainability is integrated into these parts of the organisation, the sustainability function moves from owning the questions to making them useful for others. This also changes the role: from expert to coach, from control to change leadership, and from reporting to business development.
The profession is now facing its next major shift. AI does not change the sustainability questions themselves, but it does change how knowledge work within sustainability is done. In practice, this can accelerate the integration of sustainability into the business. When less time is spent on basic tasks such as data collection, analysis and report production, more time can be directed towards the work that creates real value: interpreting what the information means, supporting better decisions and helping the organisation move forward. AI changes the conditions for all these tasks.
Information gathering can be automated, draft reports generated and deviations identified. Large volumes of data can be analysed in seconds. This does not mean that the sustainability function becomes less important. It means that the human contribution changes. As AI becomes better at producing analysis, the human ability to create meaning, make balanced judgements and lead change becomes even more important.
The sustainability function of the future will therefore not primarily compete on its ability to produce information. The real competitive advantage will instead lie in the ability to:
Most people start using AI to make their own work more efficient. That is a natural starting point and often gives quick results. But the real potential is not in individual employees saving time. It emerges when AI is used to develop how teams work, improve organisational processes and provide better decision support for senior management.
When AI is used for scenario analysis, risk modelling and strategic analysis, the sustainability function becomes an even stronger support to management. AI can help identify patterns, simulate consequences and develop decision material, while people bring judgement, priorities and direction. The more of the analysis that can be automated, the more valuable it becomes to understand what the analysis actually means — and what the organisation should do about it.
This is where the greatest business value is created. It is also where the role of the sustainability function changes the most — from specialist and reporter to strategic adviser and change leader.
What is required is a basic understanding of the technology itself — what it can do, and what it cannot do. Critical thinking about the output is also essential, because AI can be wrong, spread inaccurate information and reproduce bias that exists in the data it has been trained on.
In addition, the sustainability leader needs a good understanding of areas such as data privacy, transparency and security. Quality assurance will be an important part of the work now and in the future, and it will continue to change as the technology develops.
The sustainability leader needs to lead people through change and create psychological safety. AI will change work roles at a fundamental level, which often raises questions about professional identity and concerns about being replaced. Exposure to AI will vary between roles, but it does mean that some tasks will disappear, such as routine data collection, while others will be added, such as controlling AI-related risks and AI governance. No one can predict with certainty at this point whether the total number of working hours will decrease in the end. What is clear, however, is that new skills will be needed.
The leadership task is therefore not only to introduce new technology, but to help the team shift its professional identity from producer of material to adviser, analyst and change leader.
AI is therefore not primarily a technology issue for the head of sustainability. It is an opportunity to move the sustainability function closer to the decisions where it can create the greatest value. The organisations that succeed will not necessarily be those that use the most AI, but those that best understand what the technology should do — and what people still need to do better.