Building sustainable business operations with GenAI

Sustainability is no longer a buzzword, but something that businesses and organisations across the globe are expected to incorporate into their practices. In broad terms, it can be defined as the ability to maintain or support a process continuously over time, or as defined by the United Nations Brundtland Commission in 1987, “meeting the needs of the present without compromising the ability of future generations to meet their own needs.”

This is no easy task. For businesses, it requires expert-level balancing of several factors, including economic viability and consumption of resources alongside environmental impact and social responsibility. To make truly informed decisions around how to manage these factors, businesses need to leverage the intelligence of diverse data sources and predictive analytics – a challenge that is an excellent match for generative AI.

Making the case for GenAI investment

While many generative AI – or GenAI – tools are available and widely used by consumers, it can be a different story for enterprises. Although many business leaders already understand the value of GenAI and see its integration as a focus for their business operations, the decision to invest may not always lie with them as IT departments often make many purchase decisions across emerging technologies. In fact, recent research found that while 97% of organisations view GenAI as a priority for their business, on average across an organisation 39% of primary budget holders were IT leadership, followed by 23% of technical executive leadership and 18% of data team leadership. Business leaders therefore need to make a bulletproof case for why they should invest in this technology – particularly if their organisation holds its purse strings more tightly. Increasingly, sustainability is a highly important part of that argument.

Building smart sustainability benchmarks

The complexity of making sustainable decisions that bring value both now and in the future is one that calls for the use of multiple data sets and predictive powers. As such, legacy tools are no longer enough. These outdated systems are typically not designed to handle the vast volumes and variety of data generated today, which can lead to inefficiencies and incomplete data analysis. Using these tools, data might be stored in outdated formats, making it difficult to access and integrate with modern technologies. Such systems also lack the advanced capabilities required for sophisticated data analytics, such as machine learning and AI technologies, which are crucial for accurate predictions.

Businesses must therefore modernise their systems to leverage the full potential of data and predictive analytics, which, when used correctly, can be momentous. GenAI’s powerful abilities can more efficiently analyse vast amounts of data to identify patterns and insights that humans might miss. When this analysis is combined with predictive models, businesses can anticipate and

forecast the environmental impact of various actions, resulting in more informed decision-making and more sustainable choices.

For example, these technologies can support enterprises in creating and adhering to sustainability benchmarks. In tandem, GenAI and predictive analytics can enable the creation of smart, data-driven goals by analysing historical data and industry trends. Using this data, GenAI can simulate various scenarios to understand the potential outcomes of different sustainability strategies, helping businesses to plan ahead and establish benchmarks that are both ambitious and achievable. These technologies can also compare a company’s performance with industry standards and best practices, ensuring that benchmarks are competitive, up to date and compliant.

Once benchmarking has been locked in, organisations can then leverage predictive analytics for continuous, real-time monitoring of key performance indicators related to sustainability. And, when performance deviates from benchmarks, real-time insights and alerts can kick in to help businesses take action and maintain a good level of progress.

Optimising waste reduction strategies for increased efficiency

AI can also significantly help businesses manage waste through various innovative approaches, resulting in improved waste collection, processing, and classification. One example is AI-powered smart bins, which can automatically sort waste, minimising the need for manual sorting and boosting efficiency. Additionally, classification robots can identify and separate various types of waste, ensuring each type is processed in the most suitable and eco-friendly way.

For a future-gazing approach, predictive models can anticipate waste generation trends, helping businesses to plan more effectively. Monitoring waste levels in real-time, wireless detection systems can also facilitate timely collection and minimise the risk of overflow and related environmental hazards. Steps such as these can result in broader positive impacts for organisations, helping them reduce costs, enhance safety and lessen the environmental impacts associated with waste management.

GenAI can also gather and review data from various departments within an organisation to offer a comprehensive view of a business’s current waste reduction strategy. This intelligence can then support organisations in conducting an analysis of their waste stream, enabling them to optimise supply chain management and design sustainable products across the organisation.

Once improvements are ready to be put into action, GenAI can automate numerous processes, taking much of the work away from employees and allowing manpower to be utilised more effectively elsewhere. In addition, this approach reduces human error and enhances efficiency – a goal that businesses strive for across the board.

Keeping one foot outside of your business

Despite all its benefits, it cannot be ignored that GenAI comes with its own carbon footprint due to the significant computational resources required for training and running these models. The process of training large AI models consumes significant amounts of electricity, which is largely at present, sourced non-renewably. Furthermore, servers and data centers hosting and running AI models need to be cooled to prevent overheating, which consumes huge amounts of energy and water.

To mitigate this impact, IT infrastructure providers must step up their efforts to design and build more energy-efficient hardware. These innovations should not only be prioritised but also certified for energy efficiency, ensuring transparency and accountability. Furthermore, infrastructure providers need to offer businesses better insights into optimising the energy consumption of their data centers. As GenAI adoption ramps up globally, the overall energy consumption of data centers must be addressed, and clear strategies to reduce it must be developed.

By taking these steps, IT infrastructure providers will play a critical role in reducing the environmental footprint of AI technologies, helping businesses align with sustainability goals while continuing to benefit from GenAI's transformative potential.