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Getting started with AI for sustainability

Jonna Saarinen Senior Service Designer, Solita

Published 21 Jan 2025

Reading time 4 min

When it comes to sustainability, organisations often face challenges with incomplete or fragmented data. While they may have large volumes of data, turning this data into actionable insights can be difficult. 

This is where AI can make a significant impact. By processing and analysing vast datasets, AI helps organisations uncover valuable insights. It can also play an essential role in ensuring compliance with regulatory standards.

In my previous blog, I explored AI’s potential in sustainability. By utilising AI, companies can:

  • Drive data-driven sustainability strategies and compliance
  • Streamline reporting and stakeholder communication
  • Optimise supply chains and resource management
  • Accelerate sustainable product development

Success depends on thoughtful deployment, maximising benefits while minimising environmental impact.

In this post, I’ll explore how to establish a solid foundation before implementing AI solutions. In my next post, I’ll cover how to evaluate, prioritise, and deploy these solutions to enhance sustainability.

1. Defining goals and challenges

Before implementing AI solutions, organisations need a clear sustainability vision and goals aligned with their broader objectives. For example, if your target is achieving 50% renewable energy usage by 2030, your AI initiatives should support this goal.

It’s essential to identify key sustainability challenges before deciding if AI can help address them. Common pain points often include:

  • Keeping up with changing regulatory requirements
  • Managing time-consuming sustainability reporting
  • Measuring and reducing scope 3 emissions
  • Overcoming supply chain inefficiencies

For example, if a company struggles with tracking emissions across its supply chain, AI could be used to collect and analyse data from multiple sources to provide a clearer picture.

2. Identifying key opportunities

Once you’ve clarified your goals and challenges, you can pinpoint where AI can deliver the most value. For inspiration, let’s explore some practical use cases where AI can help organisations achieve their sustainability goals.

Strategic planning and compliance

AI can enhance sustainability strategy development and compliance:

  • Scenario planning: Build models to predict how factors like regulations and resource availability could impact your business
  • Risk analysis: Identify and prepare for risks by analysing patterns to anticipate resource shortages, regulatory changes, and climate disruptions
  • Business model innovation: Discover opportunities in the circular economy, such as sharing economy models and product-as-service offerings
  • Carbon management: Streamline emissions tracking and develop reduction strategies, from renewable energy investments to nature-based solutions

Sustainability reporting and communication

AI can streamline sustainability reporting and communication:

  • Automated reporting: Create sustainability reports that meet ESG standards and regulatory requirements while maintaining accuracy and consistency
  • Real-time insights: Provide up-to-date sustainability metrics and performance tracking
  • Stakeholder engagement: Develop compelling sustainability narratives that resonate with different stakeholders, from investors to customers and employees

Sustainable Innovation

AI can uncover hidden patterns and generate novel solutions:

  • Sustainable products and services: Analyse data to identify opportunities for sustainable products that align with customer needs, business goals, and compliance requirements
  • Materials innovation: Accelerate the discovery of sustainable materials by analyzing properties like durability, recyclability, and environmental impact to find optimal alternatives

Supply chain management & resource use optimisation

AI can bring new levels of efficiency to supply chains and resource use:

  • Supply chain management: Optimise routes, schedules, and inventory management to reduce waste and emissions
  • Energy management: Model energy consumption and identify savings opportunities, including peak usage prediction
  • Resource conservation: Enhance efficiency in resource-intensive processes, such as industrial water usage

3. Assess the quality and availability of your data

For AI to effectively advance sustainability, it needs accurate, clean, and comprehensive data. Understanding your current data landscape is crucial, even without a formal data strategy in place.

Start by evaluating your existing sustainability data across key areas like emissions tracking, energy use, and supply chains. Focus on ensuring your data is reliable, well-organised, and easily accessible to create a solid foundation for AI-driven initiatives.

Different AI approaches have different data needs. Traditional AI focuses on processing structured data (e.g., spreadsheets) to recognise patterns and make predictions, such as forecasting energy usage.

Generative AI, on the other hand, works with unstructured data (e.g., documents and reports) to analyse content and offer recommendations. For example, it can analyse sustainability reports to suggest areas for improvement.

Consider these key aspects:

  • Data quality: Evaluate completeness and accuracy across metrics. Watch for data gaps, inconsistencies, and areas where manual processes might affect reliability.
  • Data integration: Assess how well data flows between systems and departments. Identify siloed information that could prevent a complete view of your sustainability performance.
  • Historical data: Assess the depth and consistency of your data over time. Strong historical records are essential for predictive analysis and identifying trends.
  • Data security: Protect sensitive information with appropriate measures, especially when sharing data with stakeholders or across supply chains.
  • Data governance: Establish clear processes for data collection, storage, and updates. Define ownership and validation procedures to ensure data remains accurate and reliable.

Understanding these aspects helps you prepare for AI implementation and identify areas needing improvement.

4. Moving forward: From opportunities to implementation

Once key opportunities are identified, the next step is to define how to implement AI to enhance sustainability in your organisation.

In my next post, I’ll dive into how to evaluate, prioritise, and implement AI solutions to boost sustainability efforts.

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