How AI enables sustainability teams to scale with limited resources

  • Posted on December 14, 2023
  • Estimated reading time 7 minutes
Scale sustainability with limited resources

There is more evidence that shows that investment in sustainability has a direct correlation to positive business outcomes. According to Gartner, 80% of business leaders reported that their sustainability programs help to reduce their organization’s costs. Embedding sustainability in an organization’s strategy can play a crucial role in cost reduction, improving supply chains, and fostering organizational adaptability in the face of disruptions. Investments in technologies like data analytics and cloud computing have helped organizations measure and quantify their carbon impact and identify areas for improvement, and efficiencies.

There’s not always available capital to go around when it comes to sustainability initiatives, which creates an opportunity for AI to augment organizations that are looking to accelerate and improve the efficiency of sustainability solutions.

We are already seeing examples of AI helping software engineering teams be more efficient with their tasks and the trend will only increase: According to a Gartner article “By 2027, 70% of professional developers will use AI-powered coding tools, up from less than 10% today”. Between corporate social responsibility (CSR) and environmental, social, and corporate governance (ESG) goals along with mandatory regulatory reporting requirements, organizations have lots of sustainability responsibilities. Yet, sustainability teams tend to be relatively small. According to The Conference Board, a U.S. based think tank, “Two thirds of the sustainability functions have either one to five full time employees (US) or six to ten (Europe).

Enter AI.

Microsoft recently published a playbook that highlights the possibilities AI offers for sustainability and how to ensure organizations can maximize that potential. Microsoft highlights three "game-changing" abilities they forecast for AI in sustainability, and they include: the ability to measure, predict, and optimize complex systems, accelerate the development of sustainability solutions and empower the sustainability workforce.

The use cases of AI for sustainability are inspiring.

In these three scenarios, stakeholders are informed, make decisions and act faster to impact change with digital sustainability. In essence, the enabling tech fueled by data and AI provide scale, which is critical when teams are lean. These scenarios are not hypothetical. They are real world innovations that companies have invested in to impact sustainability while also driving operational efficiency, regulatory requirements, and cost savings. The companies mentioned in the scenarios above are courageous businesses that are leaning into digital to drive change.

At the core of sustainability innovation is the willingness to embrace data and AI to accelerate the ESG journey. At COP28, the United Nations Framework Convention on Climate Change and Microsoft announced a partnership that will see the creation of a “new AI-powered platform and global climate data hub to measure and analyze global progress in reducing emissions”. It’s this same sort of viewpoint of “data as an integral commodity” that the partnership Avanade brokered with the Commonwealth Scientific and Industrial Research Organization (CSIRO) was founded on. We worked with the Australian government agency to build a climate intelligence platform that provides industries with analysis and insights to inform responses to climate change, climate risk education and even custom emissions scenarios to enable organizations to comply with new and pending regulations.

The Microsoft playbook highlights the importance of data as the “…foundation on which AI operates, shaping its insights, predictions, and decision-making capabilities.” We fundamentally believe this to be true at Avanade. You can’t manage or act on sustainability unless you get your data estate to efficiently report on your goals and enable the AI-infused use cases that bring tangible value back to your business. Recent Avanade research of 3000+ professionals found that IT employees say their data and analytics platform (such as Databricks or Microsoft Fabric) is their top investment priority to scale AI in 2024. To win at the sustainability game, you must be willing to build the core data and AI components to measure, monitor and act.

Yet there’s a double-edged sword to AI with respect to sustainability. AI is energy and carbon intensive, especially when it involves large-scale data processing and complex computations. A Cornell University academic paper cites a study of training a 2022-era large language model (LLM) and how it emits at least 25 metric tons of carbon equivalents if you use renewable energy, and up 500 metric tons of carbon emissions, roughly equivalent to over a million miles driven by an average gasoline-powered car if carbon-intensive energy sources like coal and natural gas are used.

Current regulations, standards, and practices around responsible AI rarely consider the environment, and those considerations are extremely vague when they do appear. To properly address this urgent issue and help make AI systems more sustainable, organizations can focus their efforts on these three areas:

  • Seek to make software measurably efficient by optimizing processing based on energy costs and availability. The objective is to find right balance between adopting AI for its sustainability utility without contributing greater harm to the planet, and part of that starts with lowering the emissions across tech estates. Companies like Microsoft, Accenture and Avanade are collaborating with Green Software Foundation to operationalize carbon-aware software practices. For instance, we already established the AI/ML models require significant storage space and take more resources. The green software pattern to follow could be to optimize the size of the AI/ML model to save on the storage space and to take up less memory through strategies such as quantization, which essentially simplifies the sorting of information.
  • Lower the emissions across your tech estate by finding ways to monitor and report on resource usage with an end goal of optimizing energy sources, cost and usage. An example is Avanade Cloud Impact, a data platform that uses the latest in Microsoft AI and machine learning to provide technology leaders with a path to monitoring cost optimization and emissions management across their technology estate. This enables both cost efficiency and the capability to significantly reduce carbon footprint.
  • Embed sustainability considerations into your responsible AI framework so that environmental impact is a consideration alongside individual and social impacts. Organizations and their employees are not fully prepared for the integration of AI. While a significant majority of IT and business leaders (95%) express optimism about AI, fewer than half (48%) report that their organizations have implemented comprehensive guidelines for responsible AI. Implementing good responsible AI practices as part of your organization’s standard operating procedures will require substantial training and a renewed focus on digital literacy.

To attain their sustainability objectives, organizations must adopt digital strategies, which will, in turn, aid their organization’s performance. A global study conducted by Avanade revealed that organizations have the potential to generate an additional $1 billion in annual revenue and cut operational costs by over 11% by embracing a comprehensive approach to cloud technology, applications, and modern engineering techniques.

Whether your organization is already a leader in driving digital sustainability actions or if you are simply seeking out new technology to jumpstart your sustainability efforts, there are steps you can take to drive a more sustainable business with the power of data and AI by your side.

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