In 2026, most knowledge workers are using AI in some capacity, but sustainability professionals were among some of the earliest adopters. With the sheer quantity of ESG data and the speed at which trends, expectations, and regulations change, it’s clear that trying to keep on top of everything manually simply won’t work.
But the way ESG professionals (like consultants, sustainable investment or corporate sustainability teams) are using AI, and the value they’re getting from it, varies widely. For these teams, the challenge is finding the right tools and the right use cases. The ideal outcome is to leverage AI to improve the speed and scale of sustainability analysis, without losing rigor.
Why ESG workflows demand a different kind of AI
ESG professionals are inherently analytical. Teams need to assess risk, compare performance, identify gaps, and justify decisions across large portfolios, often under regulatory scrutiny and time pressure.
That work is data-heavy, but it’s also judgment-heavy. It depends on context: which frameworks matter, how materiality is defined, how internal methodologies are applied, and how trade-offs are interpreted.
We’ve spoken to some teams who have attempted to use generic AI tools like ChatGPT and Clause to assist with ESG analysis. These professionals are experimenting with these tools in other parts of their working lives and are curious to see how they handle more complex tasks.
Most have discovered that the outputs are underwhelming. Popular AI tools like these simply don’t have the context to replicate this part of the job for you. By context, we mean a clear understanding of your proprietary methodology and access to the right data to apply it consistently. Without this, any conclusions and insights generated won’t line up with your internal philosophies.
Generic tools like ChatGPT also struggle when analysis needs to be consistent, repeatable, and defensible across dozens or hundreds of companies. They may be able to analyze a handful of documents and offer insights, but attempting to do this on a broader scale, with the same consistent outputs, is all but impossible. That’s why many teams hit a ceiling when they try to stretch general-purpose tools into core ESG workflows.
Specialist sustainability AI tools are built for a narrower purpose. Instead of trying to cover everything, it focuses on a specific domain and the workflows that matter within it. In the ESG context, that means understanding regulatory frameworks, handling complex disclosures, applying defined methodologies, and producing outputs that can be compared, reviewed, and defended.
Four use cases for specialist AI in sustainability intelligence
For sustainability professionals, the real value of AI shows up in specific, high-impact workflows. These are areas where teams already spend significant time and judgment, and where scale, consistency, and traceability matter. When applied thoughtfully, specialist AI strengthens existing processes, allowing teams to go deeper, move faster, and operate with greater confidence across portfolios and clients. The four use cases below highlight where purpose-built sustainability AI can meaningfully raise the bar on how this work gets done.
1 — Pre-investment due diligence: moving faster without losing rigor
Before capital is allocated, sustainability teams are asked to form a view on risk and performance based on incomplete, inconsistent disclosures. Manual review doesn’t scale, and one-off AI prompts don’t produce reliable comparisons.
Specialist AI changes this by automating the extraction and assessment of ESG data across public filings, private disclosures, and questionnaires, all through a defined analytical lens. Instead of treating every company as a fresh exercise, teams can apply the same criteria across pipelines and geographies.
This allows diligence to move faster without becoming superficial. Outputs remain traceable to source material and aligned to internal frameworks, making them suitable for investment committees and audit trails. With better due diligence up front, sustainable investment teams can build sustainable portfolios with confidence.
2 — Portfolio monitoring and stewardship: from static reviews to continuous insight
Once investments are made, sustainability risk doesn’t stand still. Disclosures evolve, targets shift, and new risks emerge — often unevenly across a portfolio.
AI enables teams to monitor sustainability performance on an ongoing basis, rather than relying on periodic, manual reviews. By applying consistent datapoints across reporting cycles, teams can spot changes that matter, track progress against commitments, and identify where engagement is most needed.
For stewardship teams, this creates a clearer line of sight between analysis and action. Engagement priorities can be set based on evidence, not instinct, and progress can be tracked in a way that supports voting decisions and reporting obligations.
3 — Benchmarking and research: making comparisons that actually hold up
Benchmarking sustainability performance is one of the most time-consuming ESG tasks, but also one of the most powerful for persuading portfolio companies to take action or clients to adjust their strategies. Disclosures vary wildly in structure, depth, and language, making like-for-like comparison difficult even for experienced analysts.
Purpose-built AI addresses this by normalizing qualitative and quantitative data across companies, sectors, and regions. Instead of stitching together spreadsheets and summaries, teams get comparable insights grounded in the same definitions and criteria.
This turns benchmarking into an ongoing research capability rather than a one-off exercise. Teams can identify trends, peer gaps, and emerging best practices with confidence that the comparisons are meaningful.
4 — Reporting and compliance: the output, not the starting point
Reporting and compliance remain essential, but their role is changing. For leading teams, reporting is no longer the primary use case for ESG analysis — it’s the final expression of work that has already been done.
AI supports this shift by turning underlying analysis into audit-ready outputs. Disclosures can be assessed against regulatory standards such as CSRD or IFRS Sustainability Disclosure Standards, with clear explanations, source references, and confidence indicators attached to each conclusion.
This makes reporting faster and more consistent, while reducing the scramble to reconstruct decisions after the fact. When regulators or investors ask questions, teams can respond with evidence rather than interpretation.
The future of AI in sustainable investing
As sustainability expectations mature, the role of AI is shifting with them. Early adoption focused on efficiency: speeding up reporting, automating checks, and reducing manual effort. That phase isn’t over, but it’s no longer where the real advantage lies.
The next phase is about decision quality.
Sustainable investment teams are being asked harder questions by regulators, asset owners, and internal stakeholders. They need to explain not just what they reported, but why certain risks were prioritized, how judgments were made, and where trade-offs were accepted. That requires analysis that is consistent, transparent, and repeatable across time and portfolios.
In this context, AI becomes less of a shortcut and more of an analytical backbone. It helps teams surface weak signals earlier, apply their frameworks more consistently, and maintain institutional memory as portfolios grow and teams change. Instead of reacting to disclosures after the fact, teams can continuously monitor performance, test assumptions, and refine engagement strategies.
Crucially, the most effective use of AI won’t be about replacing expertise. It will be about scaling it. Specialist AI allows experienced teams to apply their judgment across far larger datasets than would ever be possible manually, without diluting rigor. As sustainable investing continues to converge with core financial decision-making, that ability will become a baseline expectation rather than a differentiator.
Putting specialist AI into practice with Manifest Climate
Manifest Climate is built to support the way sustainability professionals analyze and report on performance. It brings structure, consistency, and scale to ESG analysis across the full investment lifecycle.
With Manifest, teams can:
- Apply their own frameworks and methodologies consistently across due diligence, portfolio monitoring, benchmarking, and reporting
- Turn complex, unstructured disclosures into structured, comparable insights with full source traceability
- Monitor sustainability risks and progress continuously across portfolios, rather than relying on periodic, manual reviews
- Support stewardship and engagement with evidence-backed insights that are easy to explain and defend
- Work through a familiar, conversational interface while retaining the rigor needed for regulated, high-stakes decisions
For ESG teams, this means spending less time assembling and validating data, and more time using sustainability intelligence to inform decisions, engagement, and long-term strategy.
Book a demo or learn more today.
