Strategic AI implementation makes companies more efficient, but does it make them more sustainable? The answer is complicated.
By enabling firms to streamline workflows, process data, and monitor supply chains more effectively, AI tools have the potential to reduce an organisation’s environmental impact in logistical areas, such as transport-generated emissions and the creation of waste. However, using these AI tools consumes large quantities of energy, raising the volume of emissions from power plants to supply this increased demand.
In short, it’s a paradox. AI can be both a help and a hindrance to companies that are looking to align themselves more closely with environmental, social, and governance (ESG) initiatives.
But that is not to say AI inherently cannot be a net positive for organisations’ sustainability goals. A considered, targeted approach to AI adoption can enable firms to capture the technology’s value in managing logistical challenges, while not over-applying it in tasks where it is not needed, thereby limiting their AI-related energy consumption.
Human intuition and judgement remain essential.
Determining where AI is most useful
AI tools are able to process vast quantities of data at a rapid speed, making them highly effective in analytical roles. This includes tasks such as forecasting potential workloads, supply chain management and supplier profiling, monitoring consumer sentiments, and conducting risk assessments.
For instance, a construction company could deploy AI to analyse the quantities of materials required for a certain project, ensuring they order the correct amount of supplies. This improves efficiency, limits delays, reduces waste, and cuts down on the level of emissions from transportation needed to ship supplies to the construction site.
Alternatively, AI can be employed in the healthcare sector to monitor patient health from their homes, reducing the amount of travel required to and from hospital. This not only reduces the carbon footprint associated with travel, but also answers the social component of ESG initiatives by helping to deliver high-quality care for patients.
So, AI can work well across a range of operational roles to help companies align with ESG values. But its analytical function can also be useful in gathering and reporting data on sustainability progress and societal impact to third parties, such as investors and regulatory bodies.
These are all examples of the targeted adoption of AI tools to meet specific needs of individual organisations, and, in each case, AI is used to complete the groundwork of harvesting and presenting data, but strategic decision-making remains in the hands of human managers.
Beginning with the challenges
A mistake that many companies make when designing their AI implementation strategies is starting by looking for ways to implement the technology. This may seem strange at first, but strategic adoption requires the reverse approach.
Instead of starting by brainstorming ways to implement AI technology, the first step for business leaders should be to identify where their organisation faces challenges. Then, for each challenge, a range of possible solutions should be considered. If AI adoption presents the most efficient answer, and aligns with the company’s goals, it is at this stage that ways to implement the technology should be discussed.
Following this approach ensures that AI adoption is strategic, rather than a blanket attempt to insert AI into every area of the company’s operations, which could cause significant issues for employees (who may require additional training), costs (as systems will need to be updated), and the company’s carbon footprint (due to energy consumption) without adding significant value.
Securing value from radical transformation
AI on its own is not a universal solution to all the challenges a company faces. Successful implementation requires firms to adapt their operations and organisational structure to get the most value from investing in this technology.
For instance, if senior leaders decide to use AI to forecast and manage employees’ workflows, this may necessitate a shift in the roles and responsibilities of middle managers. Companies can become smaller and more agile by using AI rather than human labour in routine administrative processes.
This affects the governance aspect of ESG initiatives because the chain of command needs to adapt to create space for AI agents. At the same time, employees may be resistant to AI adoption if they perceive the technology as a risk to their jobs or as taking away from human interactions within the company.
So, how can companies keep employees on board during implementation? A phased approach allows time for the complex shifting that needs to occur to create space for AI agents. It also provides leaders with opportunities to communicate with employees and listen to feedback while the technology is being introduced.
Successful AI adoption nearly always triggers a radical transformation in a firm’s approach to governance – it’s not an incremental change. But, through its capacity to process and present large datasets at speed, AI paves the way for an approach to business leadership which is more in touch with employee sentiments, streamlines workflows, and drives down costs, as well as material waste.
Though AI tools require a large amount of energy to operate, with a sizeable knock-on climate impact, strategic implementation in response to specific business challenges can enable it to be a net positive for a company’s ESG targets.
