The use of AI to drive and support sustainability is undeniably promising and exciting. But while it can offer innovative solutions that help organisations to achieve and shape their sustainability goals, it doesn’t come without its fair share of challenges and scrutiny. As it becomes increasingly integral to modern business operations, many are starting to share their concerns over the impact of AI strategies on ESG commitments and the need for responsible implementation

Having previously hosted an event on AI and sustainability, we felt it was imperative to continue delving into this fascinating subject. We recently hosted a second event with a group of industry leaders to share ideas and experiences and explore some practical approaches to integrating AI in ways that support and advance their sustainability efforts.

For those unable to attend our session, we’ve summarised the main discussion points and actionable insights below so you too can learn some techniques for leveraging AI to support your sustainability initiatives.

The importance of people

A consistent and recurring theme throughout the roundtable was the critical role that people play in successful AI adoption. AI is widely considered a catalyst for innovation. Because of this, many organisations make the mistake of making it their top priority during the implementation process, when in reality, their biggest priority should be their people. Without active consideration for their people and the importance of the human factor, these organisations are more likely to face significant challenges.

Resistance to change is often cited as one of the biggest barriers to sustainable and long-term AI adoption. If employees aren’t engaged early on, they can quickly start to be fearful of the impact that AI may have on their roles, leading to concerns over job displacement and redundancy. Additionally, a lack of digital literacy and familiarity with AI tools and software could further discourage employees from embracing these new technologies, even if they could help the organisation reach their ESG goals.

As one of our attendees pointed out, many AI solutions do not align neatly with traditional and familiar procurement models. They require deeper integration into daily workflows, making adoption less about installing tools and more about rethinking how work gets done. Shifting this mindset can be particularly challenging in legacy industries, where ingrained practices and operational habits are slow to change. Another attendee noted that “Excel is still king,” and without deliberate effort, even cutting-edge AI tools risk being underused or ignored.

Executive enthusiasm also isn’t enough to drive meaningful change. The discussion touched on the extent to which successful adoption hinges on engaging frontline teams - those who interact with the systems daily. To overcome resistance, organisations need to start prioritising more people-centric strategies to help foster a greater sense of inclusion and ownership amongst their teams. This should include engaging stakeholders from the outset, by communicating clearly and transparently about the purpose and impact the adoption will have, which is vital for trust building and demystifying AI. Implementing pilot programs within teams should also be prioritised because they help to highlight the value of AI and encourage further buy-in.

Empowering internal champions was cited as a highly effective strategy for gaining buy-in. Some organisations piloted AI rollouts with open-minded teams, such as London office roadshows, before cascading adoption more widely.

The ethical and environmental impact of AI

While AI can offer a wealth of solutions to environmental problems, its carbon footprint is becoming a major cause for concern, particularly the substantial energy consumption that comes from running data centres. The immense computational power needed to run these centres, which house AI infrastructure, generate a wealth of electronic waste and consume vast amounts of water. Studies by Columbia University found that training vast language models like ChatGPT 3 can produce emissions equivalent to the lifetime emissions of five cars.

As AI adoption continues to accelerate globally, this carbon footprint continues to increase, and unfortunately, many organisations aren’t aware of the environmental implications of their AI use. To tackle this issue, greater transparency around the environmental impact of AI is desperately needed, as are initiatives that promote the use of more renewable energy resources.

The lack of clear ethical guidelines and governance structures for AI is another pressing issue. There are growing concerns over the safety of personal data being collected, stored and used by AI systems. The role of AI in decision-making has also been called into question. Algorithmic bias, which occurs when systematic errors in machine learning algorithms produce discriminatory or unfair outcomes. For example, Amazon stopped using an AI recruitment tool after discovering it systematically discriminated against female job applicants.

These challenges have prompted widespread calls for the development of ethical guidelines, comprehensive policies and governance frameworks to ensure AI is developed more responsibly. Organisations can promote more ethical and environmentally friendly AI use by establishing clear policies that promote transparency, mitigate bias and prioritise sustainability in AI development and deployment. This should include developing an AI governance strategy, investing in training and conducting regular audits.

Managing expectations

When beginning the process of leveraging AI to achieve sustainability objectives, it’s understandable that expectations can be sky high. After all, AI can seemingly do it all. However, organisations risk overestimating AI's capabilities and limitations if they don’t temper their high expectations with a realistic understanding.

This misalignment can create assumptions of a turnkey solution that generates overnight transformation with immediate success. Naturally, this can quickly lead to disillusionment when this quick win doesn’t materialise.

The reality is AI implementation of any kind is complex and requires clear objectives, collaboration, continuous learning and data governance. It should never be considered a standalone solution or short-term fix.

Data

As organisations ramp up their AI efforts, one crucial element often gets overlooked: the state of their data. No matter how powerful an AI solution is, its success depends on the quality of the data it’s built on.

Several attendees pointed to legacy systems, fragmented databases, and manual processes as significant blockers to effective AI deployment and noted that these are especially prevalent in sectors where digital transformation is ongoing.

Our attendees agreed on the value of a practical, phased approach to tackling the data challenge. Rather than attempting to rebuild everything from scratch, it can be more effective to map data by business unit, prioritising the highest-value areas, and working in manageable stages.

Varying levels of maturity

It is important to recognise that organisations are at different stages of maturity when it comes to AI. Some have made early investments that allow them to confidently embed AI into their daily operations, driving efficiency and environmental gains. Others are just beginning their journey, carefully exploring procurement options and striking a balance between automation and workforce stability.

Our guests acknowledged that AI projects often fail not because the technology doesn’t work, but because the “people journey” is neglected. Without a parallel transformation strategy that supports employees, builds digital confidence, and fosters inclusion, even well-intentioned initiatives can fall flat. Success depends not just on installing tools, but on building trust and digital capability within the workforce.

Tangible benefits

That said, AI can unlock real and measurable sustainability gains when applied thoughtfully and strategically. Several attendees highlighted AI’s potential to deliver tangible environmental improvements, particularly in sectors like real estate. Intelligent building management systems, for example, have already demonstrated significant energy savings by identifying inefficiencies such as HVAC systems running when not needed or lighting being used unnecessarily. In these cases, AI is delivering guaranteed cost reductions at scale.

Our attendees pointed to the growing value of AI in tackling complex infrastructure and operational challenges, especially in the face of labour shortages. As the pool of skilled facilities managers continues to shrink, AI is stepping in as a critical solution, ensuring performance, reducing manual oversight, and maintaining energy efficiency across large and often complex portfolios of buildings.

The discussion highlighted that AI delivers the greatest long-term impact when it’s embedded into the core of an organisation’s operations and culture and is continuously developed and improved. By doing this, organisations can not only transform how they work, make decisions and deliver services, but also enable greater and longer-lasting sustainability.

The conversation also touched on how AI may redefine the future of work. One of our attendees shared concerns about the over-reliance on tools like ChatGPT among younger professionals, with some outsourcing even basic thinking. However, the group agreed that AI can free up time for more meaningful work by taking over repetitive tasks. Looking ahead, tools integrating human-AI interaction, such as voice-activated assistants for facilities or customer service, could play a vital role in this shift.

By building it into their processes, organisations can use AI to optimise their energy use, enhance their supply chain efficiency and reduce their waste and emissions. With the right training, AI programs can also be used to support more equitable service delivery, improve workplace safety, and foster ethical decision-making. Lastly, automation can boost productivity and predict and mitigate risks. Of course, to unlock this wealth of potential, patience, ongoing management and realistic expectations are a must.

Conclusion

From energy efficiency to supply chain transparency, AI will play a major role in helping organisations achieve their ESG goals and will undoubtedly continue to become embedded in the tools and processes we use every day. There’s a whole host of things to consider when using AI to drive and support sustainability initiatives and goals, but that shouldn’t put organisations off.

If done with the required planning and consideration, AI can be a highly successful union that unlocks significant benefits, ranging from greater efficiency to reduced waste, that help drive positive environmental and economic outcomes long term. But it’s important to remember that AI implementation of any kind is always a marathon, not a sprint.

Packed with plenty of thought-provoking insights and tangible actions, our session left our attendees equipped with new approaches and greater confidence in successfully leveraging AI to advance their sustainability initiatives and goals moving forward.

If you’d like to attend one of our future events, either as an attendee or a potential speaker, you can register your interest by clicking here.