Applied AI for Sustainability Decision-Making: Recommended First Steps

The applied AI for sustainability ecosystem is vital. And unlike most large language models (e.g. ChatGPT, Claude, Gemini), these AI applications have lower resource requirements (i.e. energy & water use), but potentially higher impact in promoting sustainability solutions (i.e. energy efficiency, carbon reporting, circularity).

Sounds great, right? Well, in theory, it is. But enterprise readiness for integrating some of these new tools is just starting. It is no wonder why this is the case: any new technology requires strategic reflection, internal evaluation, readiness assessment, and gaining internal buy-in, and ultimately, budget.

While most enterprises and organizations aren’t quite ready to dive in to applied AI integration to support their sustainability programs, here are some recommended first steps to take:

1. What is the problem you are trying to solve through applied AI? Not every work flow will benefit from applied AI, so think carefully about where inefficiencies or bottlenecks exist in your sustainability work, and then consider how applied AI tools may help resolve them. Real-world example: the typical sustainability team spends up to 50% of their time on compliance. Many carbon reporting tools are developing AI features designed to significantly reduce the time allocated to compliance, freeing up team members to focus on other tasks and initiatives.
2. What is the status of your data related to the problem you are looking to solve? Data often exist in silos but need to be integrated with consistent, timely formatting and management to optimize applied AI systems. Real-world example: many sustainability teams are exploring how to use technology like AI to reduce energy consumption. Yet without access to the right internal datasets and support from other departments, data may not be “ready” for integration with applied AI systems and tools.
3. What are your existing suppliers, partners, and industry peers doing around applied AI? While there is a vital ecosystem of start-up applied AI solutions for sustainability, innovation is also happening in legacy firms, and industry peers may understand best the nuances to consider. Real-world example: waste management is a priority for companies. Simply asking existing suppliers what AI features they are developing may be a more efficient way to integrate this new AI features, versus initiating a new relationship.
4. What is the skill sets of your team members and can they be tasked with monitoring and optimizing applied AI systems? Real-world example: many sustainability teams are interested in applied AI integration but don’t have a historic working relationship with their technical colleagues. The future sustainability professional will need both subject-matter expertise and technical know-how, often acting as an interpreter between sustainability goals and technology requirements.
5. What are the costs and benefits financially to integrating these applied AI solutions? Real-world example: sustainability professionals are pressed to demonstrate the return on investment of proposed technology solutions and initiatives. External expertise can provide frameworks and market data to estimate and quantify these costs and projected benefits.

For more context on this topic, join Jeremy Tamanini for his quarterly briefing series on the state of applied AI for sustainability in 2026. To receive an invitation to the next briefing, sign up here. In advance, take a read of his paper “Applied AI for Sustainability: Opportunities for Integration.” It was published 4 months ago which feels like an eternity in this space, but still contains valuable insights and data on the topic. These insights include detail on new and legacy companies developing AI platforms and products to support enterprises in reducing scope 1, 2, 3 emissions, making carbon reporting more efficient, and promoting circularity and innovation.

Contact Jeremy Tamanini to continue the conversation on this topic, as well as reading these related recent insights from the practice:

Applied AI for Sustainability: Opportunities for Integration (link here)

AI Everywhere: Tangible Applications for Sustainability Teams (link here)

The AI Elephant in the Room (link here)

AI x Sustainability in Trump 2.0 (link here)

Remarks to the National Sustainability Society (link here)

AI in Building & Construction: Tangible Applications for Sustainability Teams (link here)

How to Work with Satellite-Based Sustainability Data (link here)