TLDR: The rapid adoption of Artificial Intelligence, particularly generative AI, by the marketing industry is raising significant concerns about its environmental footprint. While AI offers immense benefits in creativity and efficiency, its energy-intensive nature, reliance on data centers, and demand for rare materials contribute substantially to carbon emissions and resource depletion. Marketers are urged to address this hidden cost by auditing AI tools, demanding transparency from providers, educating teams, and strategically employing more sustainable AI practices to align innovation with environmental responsibility.
As the marketing industry increasingly embraces the transformative power of Artificial Intelligence, a critical and often overlooked challenge is emerging: AI’s substantial environmental cost. While AI promises enhanced creativity, faster processes, and competitive advantages, its deployment comes with a significant carbon footprint that brands can no longer ignore.
Generative AI, in particular, is noted for its immense energy appetite. Every interaction, from creating an image to drafting copy or analyzing data, triggers extensive computations on energy-hungry servers. Many of these data centers operate in regions heavily reliant on fossil fuels, exacerbating the environmental impact. According to a 2022 International Energy Agency (IEA) report, data centers, cryptocurrency, and AI collectively consumed nearly 2% of global electricity, a figure projected to double by 2026, equating to Japan’s annual electricity usage. Training a large language model like GPT-3 alone requires an estimated 1,300 megawatt-hours of electricity, with later models like GPT-4 and GPT-4o demanding even more.
Beyond energy consumption, the environmental impact extends to resource extraction and water usage. The mining of rare materials essential for AI infrastructure leads to land degradation, pollution, and significant water consumption. Data centers themselves are massive consumers of water for cooling, with larger facilities potentially using up to 5 million gallons (18.9 million liters) daily, comparable to the daily water demand of a town of 50,000 people. Noman Bashir, a computing and climate impact fellow with MIT’s Climate and Sustainability Consortium, highlights that the rapid pace of data center construction often means new facilities are powered by fossil fuels due to challenges in integrating renewable energy into the grid.
Marketers are at the forefront of AI adoption, with many expecting almost every creative project to involve generative AI within two years. However, this rapid integration presents a contradiction with growing sustainability commitments. Data from climate tech firm 51toCarbonZero indicates that while 90% of marketers believe the industry can achieve net zero by 2030, 42% admit their own use of generative AI is the single greatest barrier to this goal. Richard Davis, CEO and co-founder of 51toCarbonZero, points out that digital marketing emissions are now on par with those of the aviation industry, partly due to the rise of programmatic media, 24/7 ad operations, and generative AI.
The challenge for marketers is to harness AI’s potential without undermining sustainability objectives. Lucy Usher and Céline Craipeau of The Brandtech Group emphasize the need for the entire marketing industry to engage in this conversation, noting that once AI is implemented, it becomes very difficult to remove.
Several solutions and best practices are emerging to mitigate AI’s environmental impact:
Audit AI Tools: Marketers should assess the energy use and environmental impact of their AI tools.
Demand Transparency: Engage with AI providers about their sustainability goals and how they can help clients achieve theirs.
Educate Teams: Ensure all team members understand the trade-offs between AI use and sustainable business practices.
Lead the Conversation: Be open with clients and stakeholders about efforts to balance AI innovation with environmental responsibility.
Strategic AI Use: Utilize smaller language models (SLMs) which are leaner, task-specific, and consume a fraction of the energy of larger foundation models. Richard Davis notes that SLMs are often ‘faster, cheaper, and more than enough for many marketing use cases.’
Optimize and Design: Ironically, AI itself can support sustainability by optimizing media buys to reduce unnecessary impressions or helping design low-carbon creative assets.
While the environmental costs are significant, AI also holds potential as a tool for sustainability. AI systems can optimize resource use, reduce waste, enhance decision-making, and even manage energy grids to maximize renewable sources. However, the critical question remains whether these benefits will outweigh the energy demands of ever-growing and more advanced foundational AI models.
Also Read:
- Marketing Agencies Undergo Radical Transformation, Embracing AI to Redefine Industry in 2025
- Navigating the AI Hype Cycle in Healthcare: Industry Leaders and Media Weigh In on Practicality and Ethics
As Bratin Saha, vice president of machine learning services at Amazon, advises, ‘It’s important to get started. You can do that now by moving your data assets to the cloud, unifying those assets, and then using AI to detect patterns in those data that allow you to make smarter business decisions.’ The imperative is clear: marketers must adopt a ‘responsible-by-design’ approach, integrating sustainability into their AI strategies from the outset to build resilience and meet evolving consumer and regulatory expectations.
