As artificial intelligence becomes increasingly embedded in corporate systems, sustainability teams are under pressure to understand where AI genuinely strengthens their work and where caution is needed. To bring clarity to this topic, we spoke with Nina Benoit, Director of Sustainability at Brightest, a company that helps organizations collect ESG data, automate reporting, and streamline operations using AI-driven tools.

Nina’s perspective is relevant for businesses navigating expanding disclosure rules, limited internal resources, and rising expectations around data quality. Our discussion focused on the practical ways AI can improve sustainability workflows, as well as the environmental and ethical considerations organizations often overlook.

🎥 Watch the Full Interview: For readers who want the full context, the complete conversation is available as a recorded interview. The discussion covers how AI-enabled reporting works in practice, examples of AI agents collecting Scope 3 data, and an in-depth look at AI’s environmental footprint. Watching the interview offers additional nuance and real-world insight into how companies can adopt AI with confidence and responsibility.

Understanding AI’s Role in Sustainability

A common misconception, Nina explained, is that AI behaves like a magic wand that instantly automates sustainability work. While AI can accelerate tasks such as writing reports or retrieving data, it still depends on high-quality inputs and human oversight. AI does not eliminate the need for due diligence, nor does it replace the critical thinking required to ensure accuracy and avoid misleading disclosures.

Companies also risk overusing AI. Many organizations feel pressure to adopt AI everywhere, even in areas where simpler processes may be more effective. Nina emphasized that unnecessary AI deployment increases environmental impact without always improving results. Responsible adoption begins with understanding when AI provides genuine value and when traditional methods are more appropriate.

Where AI Strengthens Reporting and Data Accuracy

AI already plays a meaningful role in improving the reliability and efficiency of sustainability reporting, particularly when organizations work with structured datasets. Brightest’s platform allows companies to upload their ESG data, which the system then uses to generate narrative responses aligned with reporting frameworks. By pulling figures directly from verified data, AI reduces manual copying, minimizes inconsistencies, and helps teams avoid easily overlooked errors.

However, accuracy still depends on the quality of the underlying inputs. If information is missing, AI can still produce an answer, even when the answer is incorrect. In sustainability reporting, this risk is significant because incorrect statements may unintentionally misrepresent a company’s impact. Nina stressed the importance of reviewing AI-generated content and verifying that the system had enough information to produce a reliable output.

AI Agents and the Future of Scope 3 Data Collection

Scope 3 emissions remain one of the biggest challenges for sustainability teams, largely because supplier data is inconsistent, delayed, or unavailable. To help address this issue, Brightest developed an AI agent that retrieves supplier information from public sources, requests missing data, and follows up when responses are delayed.

This automation reduces the tedious and time-consuming process of chasing suppliers for information. It also standardizes the collection of emissions-related data, enabling teams to focus on analysis rather than manual outreach. For companies with large or global supply chains, AI agents offer a scalable approach that helps fill persistent data gaps.

Balancing AI Adoption with Environmental Impact

While AI can reduce sustainability workloads, its environmental footprint is often overlooked. Nina highlighted that AI systems require substantial amounts of water and electricity. Producing silicon chips demands ultra-pure water, and many data centers rely on fossil fuel-based power generation. Water is also frequently used for cooling infrastructure, compounding the overall resource footprint.

For this reason, she encourages organizations to use AI intentionally rather than reflexively. Not every task requires an AI system, especially simple communication or low-impact administrative work. Responsible use includes recognizing both the benefits and the environmental costs of AI tools.

How Teams React to AI Adoption

Internal reactions to AI vary widely. Sustainability teams facing heavy workloads often welcome AI as a practical solution that helps reduce time spent on repetitive reporting tasks. These teams tend to be more open to adopting AI quickly because it enables them to work more efficiently while meeting new regulatory expectations.

Conversely, employees in roles focused on manual data tasks may feel more hesitant. Some worry that automation will make parts of their jobs obsolete. According to Nina, resistance is less about specific departments and more about individual comfort levels and perceptions of job security. Clear communication and realistic expectations are essential for encouraging productive adoption of new tools.

Which Industries Are Moving Fastest with AI?

Nina highlighted two industries where AI adoption for sustainability appears more advanced.

Technology

Tech companies move quickly because AI is already part of their core business. They tend to integrate AI into internal processes and customer-facing products, and they typically have the budgets and expertise required to experiment early and often.

Energy

Energy companies increasingly rely on AI to model electricity demand, forecast renewable generation, and optimize resource allocation.

These applications support both operational reliability and decarbonization goals by helping operators match supply with real-time needs.

Across all sectors, leadership commitment and available resources remain the strongest drivers of adoption speed.

Practical Guidance for Sustainability Leaders Exploring AI

Nina offered several practical steps for sustainability directors and CSOs preparing to incorporate AI into their workflows.

1. Identify where your team spends the most time: Tasks such as Scope 3 data collection, report drafting, or supplier outreach typically offer the highest return when automated or streamlined.

2. Start with clear, specific needs: Tools should be evaluated based on their ability to solve your actual challenges rather than generalized promises.

3. Use AI responsibly: Nina cautioned against using AI for everything and suggested evaluating the environmental footprint of tools when possible. She also recommended sustainable AI chat options where appropriate.

4. Maintain human oversight: Even advanced AI cannot replace human judgment. Reviewing AI-generated content ensures accuracy, prevents errors, and avoids unintended misrepresentation.

Conclusion

AI has the potential to significantly improve sustainability reporting, streamline labor-intensive processes, and support companies facing growing regulatory pressure. Yet its benefits depend on thoughtful and intentional adoption. As Nina emphasized, AI should strengthen, not replace, the expertise and judgment of sustainability professionals.

Organizations that adopt AI with a balanced approach can reduce manual workload, increase reporting accuracy, and make meaningful progress toward decarbonization and compliance goals while remaining mindful of AI’s environmental impact.