The reality is that many sustainability teams are bogged down in data and manual processes. Historically, tasks like carbon accounting and sustainability data management have been crucial but tedious, requiring months of manual data collection and reconciliation processes. But now, with AI agents for sustainability, companies can employdigital labor to handle the most time consuming and data-driven tasks. This means sustainability teams can be more efficient, improving reporting and data management processes and outcomes, while also freeing up time to take more meaningful, strategic action.

Below, we break down the different types of AI sustainability agents and share how businesses are using them to augment their sustainability teams.

What you’ll learn:

• What is an AI agent for sustainability?
• What is the difference between AI agents and generative AI?
• How AI agents solve challenges in sustainability
• Types of AI agents for sustainability
• Key components of AI agents for sustainability
• Benefits of AI agents for sustainability
• Use cases for AI agents for sustainability
• How to choose the right AI agent for sustainability solution
• Implementing AI agents for sustainability: A step-by-step guide
• Future trends for AI agents for sustainability
• FAQs

AI agents for sustainability areautonomous AI agents that analyze and learn from your sustainability data to perform tasks with little or no human input. These agents enhance efficiency and effectiveness in sustainability management by leveraging advanced algorithms andmachine learning. They process vast amounts of data, identify trends, and offer actionable recommendations, streamlining operations and ensuring compliance with regulatory requirements and stakeholder expectations. The integration of AI sustainability agents reduces the burden on sustainability teams, allowing them to focus on strategic initiatives.

While both AI agents and generative AI are subsets ofartificial intelligence, they serve different goals.

AI agents can observe their environment, make decisions, and take actions to achieve a specific goal autonomously.Generative AI, on the other hand, creates new content or generates new outputs based on a set of rules or training data.

The key distinction lies in their purpose and autonomy. AI agents are created with a specific goal in mind, such as completing a task or providing assistance, while generative AI is focused on exploration and creation. AI agents use generative AI fundamentally, and both technologies complement each other in a comprehensive AI strategy.

Disconnected data: Whendata is siloed in disparate systems, you can’t achieve a holistic view of your company's sustainability performance. By usingAgentforce forNet Zero Cloud, Salesforce's sustainability software, organizations can connect sustainability data across theSalesforce Platform. Because Agentforce is an agentic layer that goes across all Salesforce applications, it extends to all parts of the business.

Growing expectations and demands:With increased interest in sustainability performance from both inside and outside of the business, sustainability teams often find themselves bombarded with questions and requests. AI agents for sustainability help you quickly find answers to your most pressing questions. This is particularly useful for completing supplier questionnaires, automating RFP processes, communicating with investors, customers, and leaders, and accelerating program planning with faster insights.

An increasingly complex regulatory landscape:Mandatory sustainability regulations are rolling out and becoming more stringent worldwide. AI agents for sustainability can expedite the reporting process by building, updating, and storing snippets for both voluntary and mandatory reports, as well as 10-K filings, aligning with specific frameworks such asESRS,SASB,CDP, andGRI. AI agents for sustainability can also automate reporting tasks, including all necessary documentation and ensuring an accurate audit trail.

Data privacy and security concerns: As AI agents collect and analyze large amounts of sustainability data,data privacy andsecurity are paramount to maintaining customer trust. It's critical to choose an application that ensures theresponsible use of AI in sustainability management. Salesforce’s internal trust product framework calls for model safety testing, human-at-the-helm product design, and in-app guidance to help with results interpretation. This framework extends to Net Zero Cloud and Agentforce, so you can maintain stakeholder trust.

Natural language processing (NLP) capabilities:NLP lets agents understand and respond to human language.

Machine learning algorithms for data analysis: AI agents use machine learning algorithms to analyze vast amounts of data, identify patterns, and make accurate predictions, driving informed decision-making.

Integration with sustainability software and business platforms: AI agents should seamlessly integrate with your sustainability software and business platforms to ensure agents have access to the necessary data to deliver streamlined operations.

Easy to customize and use: Certain AI agents for sustainability work out-of-the-box withno-code orlow-code customization, allowing teams to see results quickly. Others can be built to fit different needs and use cases.

Insights gathering: Quickly answer complex sustainability questions, providing timely and accurate information. Access real-time data to provide insights into the organization's sustainability performance, building trust and transparency with stakeholders.

Automating reporting tasks: Create, update, and adapt snippets for both voluntary and mandatory reports, as well as 10-K filings. With all documentation automatically included, significant manual burden is reduced and an accurate audit trail is ensured.

Supplier engagement: Automate responses to supplier questionnaires, improving engagement and data accuracy, ensuring supply chains meet sustainability standards. Automate the collection and verification of supplier data, reducing the burden on sustainability teams and improving data accuracy.

Stakeholder communication: Facilitate communication with investors, customers, and leaders by providing real-time, accurate data that enhances transparency and trust. Automate the generation and distribution of sustainability reports, ensuring stakeholders have access to the latest information.

Emissions intelligence:Automatically surface insights such as historical emissions, climate targets, and energy consumption.The Salesforce sustainability team is using Agentforce to deliver critical sustainability insights from the company's own data in natural language.

Field service dispatch: Optimize transportation routes to minimize carbon footprint, reducing overall emissions from delivery operations.

Sustainable sourcing: Manage sourcing and procurement with an emphasis on sustainability, ensuring suppliers meet environmental and ethical standards.

Predictive maintenance: Access machine usage data to recommend and schedule maintenance appointments before the customer has an issue with their asset, increasing maintenance efficiency.

Grower engagement and agriculture extension: ​​Streamline field application processes for farmers (i.e. fertilizers, nutrients, pesticides), enhancing company commitments to sustainable and regenerative agriculture.

1. Assess your needs: Identify the specific challenges and goals your sustainability team faces to determine the required functionalities of the AI agent. Conduct a thorough assessment of current processes, identify pain points, and define clear objectives.
2. Evaluate solutions: Research and compare different AI agents to find the best fit for your organization. Consider factors such as integration with other business technology, environmental impact, ​​capabilities, cost, and support. Evaluate available AI agents, including their features, performance, and compatibility with existing systems.
3. Prepare your data: Be sure your sustainability data is accurate, organized, and accessible to enable AI agents to deliver accurate experiences. Data preparation is critical, as the quality of the data directly impacts the performance of the AI agent.
4. Integrate AI agents with existing systems: Seamlessly integrate agents with yoursustainability software and the rest of your technology ecosystem.
5. Train and monitor AI agents: Provide the necessary training data and continuously monitor the performance of AI agents to ensure they are delivering optimal results.