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Sustainability Context
Your current involvement with ESG, sustainability, climate, reporting, carbon or related professional work.
Take a short self assessment to understand your current sustainability, AI and workflow readiness, and where Agentic AI could fit into your professional work.
Answer a few questions about your sustainability work, AI familiarity and the kinds of workflows you want to improve.
Takes about 2 minutes
This assessment is designed around the intersection of sustainability work and Agentic AI, rather than generic AI knowledge.
Your current involvement with ESG, sustainability, climate, reporting, carbon or related professional work.
How comfortable you are with generative AI and using AI to support professional tasks.
Where repetitive research, reporting, analysis and coordination could potentially benefit from AI agents.
The goal is not simply to know what AI is. It is to understand how agentic workflows can be designed around sustainability problems and professional outcomes.
For sustainability teams, the opportunity is not simply generating better text. It is understanding how AI agents can support multi-step work such as research, information gathering, analysis, monitoring and first-draft preparation while keeping appropriate human oversight in the loop.
Identify recurring sustainability tasks where an agent can help coordinate research, organise information or move work through defined steps.
Explore how agentic workflows can be structured around sustainability information, documents, data and multiple sources instead of relying on one isolated prompt.
Develop the ability to think about sustainability problems not only as reporting or analysis tasks, but also as workflows that can be redesigned with AI.
The objective is to connect AI-agent concepts with sustainability use cases. You do not need to become a software engineer to start thinking differently about how professional sustainability work can be performed.
Understand the difference between conventional generative AI interactions and workflows where agents can reason through defined tasks, use tools and progress through multiple steps.
Explore applications across ESG research, sustainability reporting, carbon and climate work, information monitoring and other professional sustainability workflows.
Learn to break a real professional problem into inputs, decisions, tools, actions, checks and human review rather than asking AI to simply produce a final answer.
Think about source quality, validation, human oversight and the boundaries that matter when AI is applied to professional sustainability work.
Translate the concepts into workflows that are relevant to the way sustainability professionals actually research, analyse, monitor and communicate information.
Build a practical mental model for evaluating where AI agents can create value in sustainability as the technology continues to evolve.
You can come from different professional backgrounds. What matters is having a real interest in applying Agentic AI to sustainability-related work.
Explore how agentic workflows could support research, reporting, monitoring, analysis and other recurring responsibilities.
Consider how AI agents could support information-heavy carbon, climate and emissions-related workflows.
Understand how sustainability information and AI-enabled workflows can intersect with finance, risk and decision-support work.
Explore ways to think about repeatable sustainability research and delivery workflows through an agentic lens.
Build enough practical understanding to identify meaningful AI opportunities without treating every AI task as an automation problem.
Develop an emerging capability at the intersection of sustainability and AI and understand where it may fit into your professional direction.
That shift is at the heart of the learning experience. Instead of chasing every new AI tool, you learn to start with a sustainability problem, understand the workflow and then consider where an agent can responsibly contribute.
The self assessment is not a generic AI test. It helps connect your current sustainability experience, AI familiarity and professional goals with the kind of Agentic AI learning you may need.
Understand whether you are completely new to Agentic AI, already using generative AI or beginning to experiment with workflows.
Think about the sustainability workflows where you would most like to save time, improve consistency or explore new capabilities.
Use your answers as a starting point before deciding whether a structured Agentic AI learning path is right for you.
Take the self assessment first. Your answers will help you reflect on your current sustainability experience, AI familiarity and the workflows you want to improve. From there, you can explore the next step for your Agentic AI learning journey.