The intersection of artificial intelligence (AI) and sustainability represents one of the most consequential developments in the private markets today, with IBM reporting that 90% of business leaders view AI as a positive force for meeting sustainability goals.
But its adoption requires a thoughtful approach: How can investors embrace its transformative capacity while mitigating broader ethical, environmental, and governance risks associated with its use?
To help investors navigate the AI frontier responsibly, this article provides an overview of the opportunities and risks associated with its use in sustainability reporting.
Where Can AI Deliver the Most Impact in Sustainability Reporting?
AI is emerging as a powerful catalyst for efficient sustainability reporting, reshaping how investors and portfolio companies approach data collection, regulatory compliance, and strategic decision-making.
1. Streamlining ESG data collection and reporting processes
In a typical sustainability reporting cycle, investors and portfolio companies are likely responding to multiple requests for data, which require resource- and time-intensive data aggregation and validation processes.
AI can help rapidly extract, structure, and unify information from fragmented sources, unlocking higher efficiency for sustainability teams. Some machine learning models can also help validate the accuracy of collected data against predefined parameters to improve overall quality. Aside from enhancing data, AI-powered predictive intelligence can empower sustainability teams with strategic insights to make more informed business and investment decisions.
Some ways investors can integrate AI into sustainability reporting processes include:
- Pre-filling ESG questionnaires by leveraging existing data to auto-fill relevant fields
- Auto-routing workflows by automatically triggering next steps and notifying relevant reviewers for approvals
- Identifying missing or inconsistent metrics to guide data remediation efforts
- Predictive data analytics and forecasting by analyzing historical data to detect patterns, trends, and anomalies for proactive risk assessments and performance optimizations.
2. Supporting compliance reporting at scale
As sustainability regulations grow in scale and complexity, AI is quickly gaining recognition as a powerful compliance enabler in the private markets. The technology can map available sustainability data to fulfill specific disclosures, flag missing information, and generate draft reports that comply with technical documentation requirements. For those operating across multiple jurisdictions, AI can help identify convergence between regulations to leverage existing data more efficiently while ensuring consistent reporting outputs.
This not only results in faster and simplified reporting across the board but also frees teams to focus on delivering strategic insights. However, human intervention is still a critical piece of the puzzle. Because AI can make mistakes, careful review is essential for producing accurate reports.
3. Transforming portfolio screening and due diligence
Beyond sustainability reporting, investors are also increasingly using AI for comprehensive portfolio screening and ESG risk assessments, enabling them to conduct a holistic examination of an organization’s sustainability profile. AI-powered tools can provide insights into how a company’s sustainability performance might evolve in response to external risk factors and industry trends. Sophisticated models may even simulate how changes in ESG risk exposure or regulation could affect portfolio resilience, supporting more informed investment and engagement strategies.
How Can Investors Mitigate the Risks Associated with AI Adoption?
Despite AI’s tangible advantages, the technology carries significant environmental, social, and governance implications. For investors committed to sustainability, practical measures can help mitigate the risks associated with AI usage:
Ensuring responsible AI governance
One challenge for investors to consider is that AI governance frameworks still need to catch up to the rapid adoption of the technology, and the responsibility of who is accountable for AI initiatives, and its risks, is often unclear in many organizations.
The most common due diligence questions center around the lack of transparency in AI-powered reasoning, ethical guardrails, and the potential data privacy violations (where confidential information is being fed into AI training models).
Investors play a role in advocating for comprehensive governance structures that establish clear accountability, ethical principles, and operational guardrails for AI use. Leveraging an investor-specific AI framework, such as the Trusted AI Assessment, also sets clear criteria for evaluating AI risks and opportunities, and shares what good looks like when it comes to responsible AI use.
Managing AI’s environmental footprint
AI requires massive computing power, driving up electricity use and carbon emissions while straining local power grids. According to new projections by the Lawrence Berkeley National Laboratory, by 2028, more than half of the electricity going to data centers will be used for AI, and AI alone could consume as much electricity annually as 22% of all US households.
Balancing decarbonization progress with AI innovation may sound contradictory, but it is not impossible. This is supported by an International Energy Agency report, which claims that widespread AI adoption “could lead to emissions reductions that are far larger than emissions from data centres—but also far smaller than what is needed to address climate change.”
Deploying AI sustainably is crucial. Investors should conduct rigorous due diligence, taking into account the sustainability implications across the AI lifecycle (data collection, training, deployment, and end-of-life), while encouraging portfolio companies to prioritize energy-efficient AI models or vendors committed to carbon-neutral or sustainable operations.
Preparing for the AI workforce shift
The rush to embrace AI comes with concerns about its implications for human workforces, amid the recent surge in AI-driven layoffs. Concerns have also been raised about how algorithmic bias can perpetuate discriminatory patterns.
Despite this, the World Economic Forum identified AI as the top driver of potential job gains by 2030. AI-specific skillsets and talents will be in high demand, and forward-thinking firms will need to bridge the skills gap to remain competitive.
Investors can support this transition through role redesigns, capacity-building initiatives, or targeted re-skilling programs. Championing human capital development not only strengthens workforce resilience and innovation capacity, but also ensures that AI augments and complements human expertise rather than replacing it.
The Path Forward for Sustainable AI Use
AI can unlock incredible value creation and competitive differentiation for a firm and its portfolios; however, the need to ground its deployment in ethical, transparent, and sustainability-aligned principles remains critical.
Navigating the complexities of sustainable AI use can be difficult, but Novata empowers investors to align innovation with intention through our platform and expert Advisory services. Reach out today to learn how we integrate responsible AI practices in our solutions and services, and how you can maximize this technology to move from reporting to strategy for better sustainability outcomes.
