Considering the environmental impact of innovation 


Editor’s note: Charlotte Hearn is business development lead at Infotools. She is based in London.

AI is silently working in the background across a variety of sectors. In health care, AI models are assisting in the prediction of patient outcomes, readmission rates and the likelihood of disease. In finance, AI algorithms are foundational to executing trades at optimal times, improving investment strategies and profitability. When we shop online, AI is enabling brands to present consumers with tailored offers.  

Market research is no exception and is undoubtedly being shaped by the continued evolution of technology. Already, insights professionals are using such tools for various purposes, including sentiment analysis, predictive analytics and chatbot interviews, with AI creating efficiencies by automating labor-intensive tasks, enhancing data analysis and providing real-time insights. While there are obvious benefits to be enjoyed, there are also some inconspicuous negatives that should be considered – one of them being the environmental impact. 

The hidden cost of AI

Artificially intelligent applications, as convenient as they may be, come with an often-overlooked environmental cost. This is probably surprising to many since there is a dearth of information surrounding the carbon footprint of AI systems. While there could be many reasons for this, The Peterson Institute for International Economics (PIIE) suggests a contributing factor to this is due to AI developers’ reluctance to share data.

But how big is this energy consumption problem? It’s estimated that training large language models can use the same amount of energy as it takes to power 130 homes for a year. A study by Hugging Face and Carnegie Mellon University found that producing one generative AI image uses the equivalent energy of a single smartphone charge. Then there’s the likes of Microsoft, Amazon and Google, each struggling with increasing energy demands from AI. Remarkably, Google is facing a tough challenge in meeting its carbon neutrality goals and has seen its greenhouse gas emissions grow by 48% since 2019 (subscription required).

The issue of hidden costs is compounded by the ways in which many organizations are outsourcing the use of AI – they’re receiving all its outputs and benefits while being far removed from the consumption and interaction with these platforms. As far as researchers are concerned, the challenges surrounding AI adoption continue (such as data quality, bias, privacy and transparency) yet the impact of AI on sustainability is too often forgotten. 

AI and the double-edged sword of sustainability

Balancing AI's benefits with its environmental costs is a challenge not only for the tech giants. It’s one that researchers must address, too. As AI technologies continue to evolve, their impact on the environment is a crucial consideration. 

AI has the potential to contribute significantly to environmental conservation efforts by optimizing energy consumption, reducing waste and improving resource management. Such systems can analyze vast amounts of data to identify patterns and inefficiencies across supply chains. However, the environmental impact is double-edged. The computational power required to train large AI models, such as those used in natural language processing and deep learning, is immense. As the tech giants have found, it often involves extensive use of data centers, which consume significant amounts of electricity and contribute to carbon emissions, with a study by the University of Massachusetts having found that training a single AI model can emit as much carbon as five cars over their lifetimes.

To address these concerns, we must all explore ways to reduce AI’s carbon footprint. After all, consumers are demanding sustainability from the companies with which they do business. The environment is a top concern that spans generations, with PWC finding that consumers are willing to pay a 9.7% sustainability premium even as inflation and cost of living continue to bite. Consumers are looking for companies with sustainable practices, and this trickles down to every business function – including insights. 

AI and sustainability: The need for a thoughtful, balanced approach 

Awareness of environmental impacts will continue to grow as AI is ever more widely implemented. Our goal must be to harness its potential to create a more sustainable future while minimizing its environmental footprint. 

To this end, there is an acute need for a considered approach. We must focus on ongoing human oversight, ethical considerations and sustainability when taking advantage of the efficiencies AI has to offer. Take IBM's New Chief Sustainability Officer Christina Shim. She has stressed that more companies must think about their strategy intentionally including an assessment of “all the implications” rather than simply “using a sledgehammer to hit a nail.”

Responsible AI: Marketing research industry initiatives 

Market researchers are tasked with the study of human behavior, even as the landscape around us changes rapidly. Attitudes, behaviors, needs, circumstances and preferences are changing so fast that it can be hard to keep up.

As AI becomes more integral to the industry, it is crucial to consider its environmental impact. Even in marketing research itself we can make choices that mitigate the carbon footprint associated with these technologies. 

To do so, we must ask the right questions of vendors, seek energy-efficient models and leverage renewable energy sources. By integrating AI responsibly, the industry can support a sustainable future while harnessing the transformative power of technology. It’s also important to encourage knowledge sharing and collaboration and to explore and promote industry initiatives such as the climate pledges issued by the Market Research Society and ESOMAR.

AI is a tool that can augment, rather than replace, human intelligence. From automating data collection to providing real-time insights, such technologies are streamlining processes that were once labor-intensive and time-consuming. Integrating it effectively, ethically and sustainably, however, requires human skills such as creativity, empathy and critical thinking. Market researchers have an opportunity to shape the future of the industry by harnessing the power of AI-driven technology thoughtfully. Hopefully, we can help to fight climate change at the same time.