- Defining goals and challenges
- Identifying key opportunities
- Assessing data quality and availability
Read the previous blog post: Getting started with AI for sustainability
Now, let’s explore how to move from planning to action. I’ll focus on three phases that will help you integrate AI into your sustainability efforts:
- Exploring available AI solutions and finding the right fit
- Evaluating and prioritising solutions based on value and feasibility
- Developing and scaling solutions
1. Explore solution options
AI offers a range of solutions to various sustainability needs:
Predictive analytics tools: Major cloud platforms like Microsoft Azure, AWS, and Google Cloud offer both general analytics and specialised sustainability solutions. For example, Microsoft Sustainability Manager helps track environmental impact, while Google Cloud’s Carbon Footprint focuses on emissions monitoring.
Generative AI platforms: Tools like Claude or ChatGPT can analyse vast amounts of data to generate valuable insights and recommendations. For example, they can help analyse regulations to suggest tailored sustainability initiatives, or assess supply chain data to identify sustainability risks and opportunities.
Specialised sustainability software combine AI with domain expertise:
- ESG management and reporting: Tools like Workiva simplify ESG reporting through integrated AI capabilities. By automating analysis and documentation tasks, teams can quickly create and edit content and generate insights. For example, the platform helps teams create an ESG double materiality assessment or draft a climate impact analysis.
- Carbon accounting: Solutions like Persefoni help companies track and reduce their carbon emissions using AI. Persefoni converts your business data – like electricity usage or travel – into carbon footprint numbers. It can also spot unusual patterns and provide technical support when needed. This makes carbon accounting faster and more accurate, letting teams focus on actually reducing emissions rather than just measuring them.
- Life cycle assessment (LCA): Tools like Makersite apply AI to analyse product environmental impacts throughout their lifecycle. This helps companies understand and optimise their products’ environmental footprint from raw materials to end-of-life. For example, Microsoft used AI-powered LCA to spot improvement areas in their supply chain, cutting the Surface Pro 10’s carbon footprint by 28% in two years.
- Supply chain risk management: Tools like Prewave use AI to help companies understand their entire supply chain, from direct suppliers to their partners’ partners. The system combines public information like news with private supply chain records and shared customer insights. This helps companies spot potential issues early, ensure compliance, and keep operations running smoothly even if unexpected challenges arise. For example, Kärcher, a cleaning equipment manufacturer, used Prewave’s AI to reduce their compliance monitoring time by 40x.
For organisations with unique requirements, custom solutions through enterprise platforms offer maximum flexibility. This means building tailored applications using platforms like Microsoft Azure, AWS, or Google Cloud that can combine multiple AI capabilities.
Often, a hybrid approach—combining ready-made tools with custom solutions—can deliver the most comprehensive results. For example, you might use specialised carbon accounting software for emissions tracking while developing custom AI models for your unique supply chain optimisation needs.
Example from Persefoni: Converting business data into carbon footprint numbers. Here, AI automatically matches different types of business activities with their appropriate emission factors to calculate the carbon impact.