Leading asset managers are adopting AI to analyze sustainability disclosures at scale. For several years now, asset managers have been looking for workarounds that deal with a hard truth: ESG data is essential for investment decisions and stewardship, but sustainability reporting is inconsistent, fragmented, and time-consuming to analyze manually. If assessing via a custom framework, teams are expected to synthesize disclosures across hundreds or thousands of holdings, often buried in long PDF reports or regulatory filings. If outsourcing to third-party data aggregators (ESG ‘scores’), the burden of manual research is lifted, but teams are getting only superficial data with little transparency.
This challenge is intensifying as sustainable investing scales. According to the Global Sustainable Investment Alliance, global sustainable investment assets reached over USD 30 trillion, making reliable and repeatable ESG analysis a core operational requirement rather than a niche capability.
In 2026, AI is sophisticated and reliable enough to offer asset managers a new approach to ESG screening and analysis. The AI tools available to asset managers are not shortcuts around judgment or expertise. But the smartest asset managers are using them to close the gap between trawling through files and making a decision, tasking these models with mirroring their own analytical approach to assessing and reviewing potential and existing portfolio companies against pre-defined sustainability criteria.
Why sustainability analysis breaks down at the portfolio scale
Sustainability reporting is fundamentally unstandardized. Even when companies follow the same frameworks, disclosures vary in depth, structure, and clarity. Studies analyzing corporate sustainability reports using natural language processing show how much critical ESG information sits in unstructured narrative text rather than standardized tables. For an asset manager evaluating ESG performance across hundreds of companies, this creates compounding friction.
Each sustainability or annual report can run dozens or even hundreds of pages. Analysts must manually locate relevant sections, interpret narrative language, reconcile inconsistencies across years, and normalize disclosures into internal formats. Over time, this introduces drift, where similar companies are assessed differently simply due to time pressure or reporting style.
Generic ESG datasets rarely solve this problem. They are built to be broadly applicable, not aligned to a firm’s specific stewardship priorities or risk thresholds. Teams often end up reverse-engineering ratings or manually supplementing them with additional review, which undercuts efficiency.
Why generic AI tools fall short for asset managers analyzing sustainability
Many sustainability teams are already experimenting with AI through general-purpose tools like ChatGPT, Claude, or Copilot. These tools can summarize documents or answer isolated questions, and can be helpful to sustainable investment teams in some capacities, but when it comes to the core of the job — portfolio-level analysis — they struggle with the demands.
Generic tools don’t retain a firm’s internal methodology, apply consistent logic across hundreds of companies, or produce outputs that can be re-run, audited, and defended months later. Making small changes in prompts or context can lead to materially different results, which is unacceptable in regulated investment environments.
Some teams attempt to solve this by building AI tools in-house. While this offers customization, it often comes with high opportunity costs and ongoing maintenance challenges. Without sustained investment, these tools can quickly become brittle or outdated, and many teams find it’s simply not worth the effort.
There is a better, third way: instead of generic tools like ChatGPT, or in-house tools that often cost more than they return, asset managers can turn to AI tools specifically built for sustainability analysis.
How leading asset managers integrate AI into ESG analysis
Asset managers that get real value from AI focus on selecting the right tool for the job, and then fully integrating this tool into their existing workflows. They integrate AI into structured assessment pipelines designed around their own investment and stewardship logic.
Below are four key workflow points that can benefit from the help of specialist AI. Leading asset managers are narrowing in on these points to save time and get deeper, more accurate insights into sustainability performance.
1 — Use AI to ingest and standardize sustainability data
AI can rapidly extract relevant ESG information from sustainability reports, annual reports, regulatory filings, and survey responses, even when these appear in inconsistent or unstructured formats. Natural language processing techniques allow teams to process large volumes of narrative text far faster than manual review.
NLP-based approaches can surface recurring themes, material topics, and disclosure patterns across large collections of sustainability reports, enabling analysis that would be impractical manually.
💡Manifest Climate allows asset managers to analyze sustainability performance based on publicly available disclosures, as well as private documents uploaded by users.
2 — Use AI to map sustainability disclosures to internal frameworks
Extracted data is useful only when aligned to internal expectations. Advanced ESG analysis uses AI to classify disclosures against a firm’s own frameworks, risk thresholds, and stewardship criteria rather than relying on generic scoring systems.
Machine learning models can align narrative disclosures to structured ESG dimensions with greater consistency than manual tagging, meaning that AI tools are more suited to this particular part of the job than their human counterparts. This allows asset managers to apply the same logic across companies, even when reporting styles vary, and focus on analysis rather than data prep.
💡Manifest Climate allows asset managers to assess sustainability performance against their own proprietary frameworks, as well as against key regulatory requirements and standards, such as CSRD or ISSB.
3 — Use AI for sustainability benchmarking, gap detection, and anomaly analysis
Once disclosures are structured, AI can compare companies against internal benchmarks, peer groups, or regulatory expectations. This includes identifying missing disclosures, weak evidence, or inconsistencies between stated commitments and reported actions.
This type of analysis helps teams focus attention where it is most needed.
💡Manifest Climate gives asset managers high-level scores of disclosure quality and coverage, flagging key areas for attention. It allows users to benchmark companies against one or many similar companies.
4 — Use AI for portfolio-level sustainability insight and reporting
The biggest change for asset managers comes when they get access to specialist AI tools that aggregate findings across portfolios rather than analyzing companies in isolation. Patterns emerge that would be invisible through manual review, such as recurring disclosure gaps across a sector or systemic weaknesses in transition planning.
AI-enabled sustainability analysis can improve reporting consistency and decision relevance when applied at the portfolio level, and these insights then feed directly into engagement strategies, escalation decisions, and investment committee discussions.
This is not just a one-off exercise. The best AI tools will monitor sustainability performance at the individual asset and portfolio level, flagging issues for further follow-up or engagement.
💡Manifest Climate gives asset managers a portfolio-level view of sustainability performance, allowing automated ESG monitoring and flagging areas for active engagement.
Why this matters now
Stakeholder expectations are rising across markets. The role of the sustainable investment team is moving from the periphery to the centre of risk management and business decision-making.
At the same time, sustainability data volumes continue to grow faster than teams can manage manually. Without AI, the gap between expectations and capacity will widen.
The smartest asset managers are using specialist AI tools to conduct human-quality analysis at a scale only machines are capable of. With their custom frameworks driving the models, sustainable investment and stewardship teams can spend less time in data collection mode and more time interpreting results, making complex judgment calls, and engaging with portfolio companies to improve sustainability performance.
How Manifest Climate helps asset managers gain a competitive advantage
Manifest Climate is built for teams that need to analyze sustainability disclosures at the portfolio scale without sacrificing rigor or judgment.
Instead of forcing asset managers to adapt their workflows to generic ESG datasets or brittle AI tools, Manifest lets teams bring their own methodologies into the analysis. You define what matters. The platform applies that logic consistently across disclosures, companies, and portfolios.
In practice, teams use Manifest to:
- Map sustainability disclosures to bespoke stewardship and risk frameworks
- Analyze large volumes of unstructured reporting without manual review
- Generate traceable, auditable outputs that stand up to internal and external scrutiny
- Surface portfolio-level insights that inform engagement, escalation, and governance decisions
For asset managers facing growing disclosure volumes and rising expectations, Manifest Climate helps turn sustainability analysis from a bottleneck into a strategic input.
If you want to see how this works in practice, you can explore the platform or book a demo with the Manifest team.
