Interview Series: Grace Sai

This week’s read time: 5 minutes

Welcome to the Green Digest Interview Series, our weekly feature showcasing conversations with the industry’s leading voices - CSOs, sustainability directors, and other senior professionals shaping the sustainability landscape. Each edition dives into their professional journeys, hands-on insights, and outlook on the challenges and opportunities defining corporate sustainability.

These interviews are designed to be quick, insightful reads, offering you actionable takeaways and a personal glimpse into the people leading the way. Stay tuned for stories, strategies, and lessons that matter to you.

PROFILE

This week’s guest:

Grace Sai

Co-Founder & CEO at Unravel Carbon

Grace Sai is the Co-Founder and CEO of Unravel Carbon, an agentic sustainability advisory platform that combines human expertise and AI agents to maximize sustainable business outcomes. Grace built and exited two companies before founding Unravel Carbon, where she spent a career building purpose-led ecosystems, platforms, and communities, and is now turning that same instinct toward closing the gap between climate ambition and action. She is an IMAGINE Leader, Kauffman Fellow, and Skoll Scholar.

Today, Unravel Carbon is a globally recognized company that works with customers across 60+ countries and has built a suite of domain expert AI agents focused on supply chain data collection, transformation, product carbon footprinting, gap analysis, and sustainability insights.

How does AI move sustainability from reporting into real operational and financial impact?

When we started Unravel Carbon 4 years ago, we knew carbon profiling was the first necessary step for actual decarbonization, climate risk, and supply chain resilience to take place. Because what isn’t measured doesn’t enter decision-making, and what doesn’t enter decision-making doesn’t change. But we did not expect Chief Sustainability Officers (CSOs) and their teams to have such a capacity crisis - 91% of global GDP is exposed to at least one sustainability regulation. Therefore, we decided to be AI-native from day one, as a CSO’s real-time, continuous, and contextual decision-making layer.

Today, our suite of eight AI agents is unlocking use cases beyond the sustainability reporting team. Over 50% of our customers have organically adopted these agents into their workflows. Teams upload sustainability reports, supplier invoices, utility bills, and raw Excel files in different languages. The agents read them, understand the structure, map the data, calculate, and audit themselves.

Examples of use cases include:

• An enterprise team ran 241 sessions across four agents, transforming messy multi-site waste and water data across Australia and New Zealand into platform-ready emissions data.
• A Japanese manufacturing conglomerate queries Scope 1 and Scope 3 data entirely in Japanese, comparing factory emissions and interrogating decarbonization levers.
• A global conglomerate uses our Copilot to audit emissions discrepancies, draft defensible sustainability narratives, and pull monthly breakdowns across reporting periods for actionable plans. 

These are just a few examples where our agents solve problems spreadsheets can’t, but the human remains the ultimate decision-maker. We like to say ‘let agents be agents, and humans be humans’. In fact, we believe when built right, agents allow us to be ‘more human’.

Can you walk us through a case where your solution directly influenced a decision?

A good example is IKEA. Their data analytics team used our Product Carbon Footprint (PCF) Agent on the bill of materials for their famous Billy bookshelf and asked for maximum optimization of the product’s carbon intensity. The agent suggested a set of ideas, and the team then verified them. They concluded that ideas corresponding to a 62% reduction potential were legitimate. That is exciting because it moves sustainability away from reporting and into actual product decisions.

Another example is with ABB, the world’s #1 motor manufacturing company. They too, use our PCF Agent within their R&D and design labs. Historically, their product design was solely focused on cost and performance. Now they’ve added a third dimension: environmental impact. That matters because about 80% of a product’s carbon footprint is decided at the design stage. If we miss that moment, we miss a major compounding opportunity to decarbonize the economy, one product at a time.

IKEA has also used our data collection or supplier engagement agent to start collecting data beyond Scopes 1, 2, and 3 from hundreds of suppliers. In the old way, supplier response rates are often very low, and project management became a huge burden. The agent changes that dynamic by making the process much more flexible, educational and much less manual.

Your examples suggest the real shift is not just automation, but a different way of working. What do your agents actually do across the workflow?

The Sustainability Copilot is like a sustainability assistant that is always with you. It is trained on our expert-curated knowledge base of climate science, frameworks, and standards like the GHG Protocol, ISO, and product carbon footprinting methodologies. It has a customizable knowledge vault for your company-specific files, peer benchmarking, and a gap analyzer against major compliance frameworks. Then, the Data Transformation Agent allows users to convert messy, raw files, like bills, spreadsheets, and more, into structured business activity data for calculation. The Data Collection Agent supports a dynamic workflow for gathering sustainability information internally and externally, with guidance, prompting, and reminders. Our Product Carbon Footprint agent can generate a PCF report for a product with thousands of components in about 15 minutes and supports what-if analysis and scenario modeling. We also have review, audit, and insights agents across workflows. The goal is not a generic AI wrapper. It is to create agents that are deeply infused with sustainability workflows and climate domain knowledge. Our vision is where each sustainability professional is supported by an Unravel agent.

Where are clients seeing the clearest ROI from this today?

The biggest ROI is that they reduce their cost of inaction. In the past, teams often had two poor choices: either pay for very expensive manual consultants or adopt complex software that still lacked continuous domain expertise or context. So a lot of organizations simply got paralyzed and did nothing more than once. Agents change that.

For some of the workflows I mentioned, we charge by consumption rather than with large license fees or seat-based models. If you have 40,000 product components to assess, you pay for 40,000. If you are engaging 1,000 suppliers, you pay for 1,000 suppliers. That lowers the barrier to getting started.

Also, speed to value matters. In sustainability, feedback loops are often so long that teams struggle to see the payoff from the beginning. People need to see the end result in their mind before they start. Our agents help make that possible.

You’ve spoken about how today’s economic systems are still shaped by outdated constructs like GDP and quarterly earnings. How does that influence how you think about building a sustainability company today?

The world today is still governed by constructs like GDP and quarterly earnings, and both were created in a very different era for very different motivations. They were designed at a time when capital was the most scarce and valuable resource, so naturally everything was optimized to monitor around it. But today, what is truly scarce is the balance of our natural ecosystem.

The issue is that GDP, for example, captures growth but ignores the destruction created in the process. That creates a structural mismatch between what we measure and what actually matters.

I think we are moving toward a world where this starts to change, where regeneration and net positive impact become part of how we evaluate progress. But for us and all sustainability leaders, that means accepting that this is a long and difficult journey. The feedback loops are long, and the results may not always be immediate, but if you want to help redefine systems that were not built for today’s world, then this is the work to do. It is our goal to make that work a little easier.

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