AI, Information, and the Environment: Balancing Automation and Sustainability in the Age of GenAI

Earth Day 2025: Intelligent Information Management and Its Growing Power Requirements

Earth Day is an excellent opportunity to pause and reflect on our power needs in our private lives and businesses, particularly the growing and changing needs triggered by Generative AI (GenAI) technologies.

For professionals in information management, especially those leveraging Intelligent Document Processing (IDP) and content automation tools, GenAI offers transformative possibilities: faster data extraction, improved decision-making, and streamlined workflows. These enhancements can provide significant value for organizations, including tangible returns. However, these benefits come with real environmental costs—from energy-intensive model training to water-hungry data centers.

An Overview of AI Energy Requirements

Recent studies highlight the substantial energy requirements of training and operating AI models. It is important to note that these assessments are based on various assumptions related to the language model and methodology used, the complexity of prompts etc. However, keeping this in mind, it is worthwhile to bring a couple of recent data points to our attention:

• According to a paper from MIT, researchers have estimated that a ChatGPT query consumes about five times more electricity than a simple web search. Other data points suggest a 10x ratio.
• An assessment by ADaSci highlights the impact of the size of an LLM on its energy consumption. The training of GPT-3, which has 175 billion parameters, consumed an estimated 1,287 MWh of electricity. Newer models with expanded capabilities are considerably larger; for example, GPT-4 has 1.76 trillion parameters. ClinicalBERT however, a medical specific LM, has only 110 million parameters.
• Goldman Sachs Research forecasts the required power for data centers to grow 160% by 2030. This will likely cause their carbon dioxide emissions to at least double between 2022 and 2030.
• An assessment published by an Associate Professor at the University of California, Riverside, details the water consumption of LLMs. He notes that GPT-3, a relatively small model, consumes approximately 500 milliliters of water for every 20-50 queries, depending on when and where the model is hosted.

Understanding Responsible Innovation

As business leaders integrate AI, particularly GenAI technologies, into their IIM and IDP strategies, they need to include an important question in their decision-making process: How do we innovate responsibly, balancing productivity and experience with environmental sustainability?

This is a multi-faceted question that requires an in-depth analysis of factors that play a role in the innovation and automation of business processes. When decision-makers analyze automation opportunities, they consider their impact on operational cost, customer satisfaction and loyalty. When they assess the expected ROI of an investment in an expanded or new solution, not only is the upfront investment an important parameter, but so is the total cost of ownership. Beyond the core cost aspects, there are also the “softer” parameters, like customer satisfaction and loyalty, which need to include an assessment of the cost to attract new customers compared to those required to maintain an existing customer relationship.

Calculating the Total Cost of Sustainability

Now let’s add the sustainability dimension to it, which is often not integrated but part of a siloed assessment of carbon footprint and strategic considerations for their reduction. Maybe it is time to establish a new framework of “total cost of sustainability” integrated in these investment decisions.

Since GenAI technologies started to enter mainstream availability 2.5 years ago, organizations have moved from the initial excitement to realize that a solution with positive ROI requires careful considerations including aspects like:

• Which processes present a significant opportunity for automation from a business value perspective
• For which use cases and input types do GenAI powered IDP solutions offer a material improvement over established IDP solutions
• Which language models and methods meet the requirements for domain-specific and organization-specific knowledge and expertise
• Where can multi-modal capabilities of language models support the removal of barriers associated with siloed processes established for different input types and channels
• Where can agentic AI capabilities remove mundane tasks like validation of extracted data, risk assessment, fraud prevention etc.

All of these considerations impact operational cost, customer experience, and employee experience.

Sustainability Considerations

Now let’s add the sustainability dimension and arrive at the following considerations:

1. Compute intensity: When automating workflows, ensure to optimize the process, not just digitalize it keeping the inefficiencies. Reflect compute intensity in your workflow design.
2. Lifecycle Thinking: Consider the full lifecycle of business inputs, taking into consideration its end-to-end process from capture and processing, validation and decision-making to storage and retrieval. Each stage impacts your energy footprint.
3. AI Technology Impact: Select IDP and IIM solutions that leverage AI and GenAI technologies based on their business impact; not all GenAI-infused solutions warrant the cost and the environmental impact for your use case and requirements.
4. Model Mindfulness: Choose the smallest, most efficient model that gets the job done. Fine-tuned SLMs and domain-specific transformers often outperform general-purpose LLMs in IDP workflows—and they come with lower carbon costs.
5. Renewable Energy: Select data centers that prioritize renewable energy sources to mitigate carbon emissions.
6. Partner Selection: Partner with vendors that commit to renewable energy-powered cloud infrastructure and transparent emissions reporting.

Image Source: Copilot, 2025

Conclusion

As we recognize Earth Day 2025, it’s time to elevate the conversation: information is power—but it also consumes power. AI-driven document processing has immense potential to improve accuracy, speed, and customer experience, but we need to balance our digital efficiency with its cost to the environment. Information professionals and enterprise leaders need to align their technology strategy with environmental responsibility.

The integration of GenAI into business operations offers transformative potential but also brings environmental considerations to the forefront. By carefully evaluating the trade-offs between cost, productivity, customer experience, and sustainability, organizations can make informed decisions that align with both business objectives and environmental commitments.

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