Environmental, social, and governance (ESG) data has become a cornerstone of financial decision-making. Yet for many investors and consultants, the sheer scale and complexity of available information is creating more confusion than clarity. Disclosures often lack detail, differ across frameworks, or lean too heavily on marketing claims. The result: capital is misallocated, risks are mispriced, and firms face reputational and compliance headaches.
This is not a niche problem. In 2022, sustainable investment assets surpassed USD $30.3 trillion globally, according to the Global Sustainable Investment Alliance. That figure shows how deeply ESG factors are now embedded into capital markets. But with more capital at stake, the need for accurate and comparable information has never been greater. If the data underpinning these decisions is unreliable, both investors and consultants risk making flawed calls that can ripple through markets.
The consequences of poor-quality ESG data include:
- Financial risk: portfolios may hold overexposed assets that face transition risks or stranded value.
- Compliance risk: companies might fall short of emerging regulations like the Corporate Sustainability Reporting Directive (CSRD) or California’s climate disclosure laws.
- Reputational damage: firms can be accused of greenwashing if disclosures do not stand up to scrutiny.
Investors want reliable data to assess exposure and opportunity. Consultants want efficient tools to give their clients actionable insights. Both need better systems to move beyond today’s fragmented landscape.
The good news is that new approaches, especially artificial intelligence (AI)–powered solutions, are turning ESG data from a reporting burden into a strategic asset.
ESG data challenges are growing
The first challenge is volume. ESG data now comes from corporate sustainability reports, annual filings, regulatory submissions, ratings providers, NGOs, and real-time media coverage. Each year brings more frameworks and disclosure rules. The European Union’s CSRD alone requires thousands of companies to report against detailed European Sustainability Reporting Standards (ESRS). In the United States, California’s SB 261 requires climate-related financial disclosures.
While these regulations increase transparency, they also overwhelm the teams trying to make sense of them. The World Business Council for Sustainable Development found that 87% of companies feel burdened by overlapping disclosure frameworks and many struggle to produce consistent reports across jurisdictions.
For investors, data quality influences capital flows, valuations, and risk management. If material information is missing or unreliable, portfolios can be exposed to transition risks or stranded assets.
For consultants, advising clients without a clear view of ESG performance is a competitive disadvantage. Weak or outdated data makes it harder to benchmark peers, identify compliance gaps, and deliver credible guidance for clients.
Disclosure expectations continue to multiply. Already, 71 stock exchanges worldwide provide ESG reporting guidance to listed companies (IFC Beyond the Balance Sheet). Stakeholders like NGOs, employees, and consumers also demand transparency on climate, labour practices, and governance. Investigations can surface faster than annual reports, leaving companies exposed if their data is not validated or up to date.
In short, ESG disclosures are multiplying, but without scalable ways to organize, compare, and validate them, the result is noise rather than insight.
3 critical ESG data gaps
1. Data gaps and omissions
Data gaps occur when disclosures are incomplete, outdated, or unverified. Commonly missing information includes Scope 3 emissions, climate risk strategies, and supply chain impacts — all of which are factors that investors need to assess long-term exposure.
Scope 3, in particular, is a sticking point. For many companies, it represents more than 70% of their total carbon footprint, yet it is also the hardest to measure. It depends on supplier disclosures, customer usage data, and often opaque global supply chains. Many firms rely on estimates or proxies, which reduces comparability.
Other forward-looking data such as scenario planning or transition strategies is also scarce. Companies may disclose today’s emissions but omit whether they have a credible path to decarbonize.
These omissions lead to flawed portfolio analysis and unreliable client reports. Investors risk mispricing exposure, while consultants risk basing recommendations on incomplete data. Both can be accused of misrepresentation if gaps are not flagged and addressed.
2. Reliability and greenwashing risk
Another major challenge is reliability. Some ESG disclosures resemble marketing material more than audited fact. Without independent assurance, claims about carbon neutrality, diversity initiatives, or community impacts are difficult to trust.
For investors, this raises financial risks. Portfolios built on unreliable data may overestimate resilience or underestimate regulatory exposure. For consultants, the reputational stakes are equally high. Advising clients based on unverified claims reduces credibility and could expose firms to liability if greenwashing allegations arise.
Greenwashing has moved firmly into the enforcement arena. In 2023, the European Commission fined several companies for misleading environmental claims in consumer advertising, and regulators in the United States and Asia launched investigations into financial firms that overstated sustainability credentials. These cases highlight why assurance and validation matter.
3. Inconsistent disclosures
Perhaps the most frustrating challenge is inconsistency. Companies report ESG information under a patchwork of frameworks, each with its own definitions and methodologies.
For example, the Global Reporting Initiative (GRI) emphasizes broad stakeholder impacts, while the Sustainability Accounting Standards Board (SASB) focuses on financial materiality. The Task Force on Climate-related Financial Disclosures (TCFD) prioritized governance and risk management. Meanwhile, the CSRD is bringing mandatory alignment with ESRS, and the ISSB is pushing for global comparability. Companies increasingly face the reality of needing to comply with more than one disclosure requirement.
Even when the same metrics are disclosed, they are often presented in different formats, levels of detail, or timeframes. This makes apples-to-apples comparisons nearly impossible.
The inconsistencies are magnified across supply chains and in emerging markets, where ESG disclosure requirements may be weaker or nonexistent. Historical data can also be scarce, making it hard to track progress over time.
For investors and consultants alike, inconsistent disclosures mean analysis takes longer, requires more assumptions, and delivers less confidence.
How to leverage AI to overcome ESG data challenges
AI is proving to be the breakthrough that makes scalable ESG analysis and assessment possible. Instead of relying on manual reviews, AI systems built for ESG can process vast amounts of structured and unstructured information, standardize it, and flag anomalies in real time. Key capabilities include:
- Automated extraction of ESG information from public and private data sources
- Intelligent mapping across frameworks like TCFD, CSRD, ISSB and more
- Normalization of disclosures to create comparable datasets
- Anomaly detection to flag potential errors or misleading claims
- Predictive analytics to surface forward-looking risks and opportunities
- Real-time integration for ongoing portfolio monitoring
Together, these tools transform ESG data from noise into a reliable foundation for decision-making.
Streamline ESG data collection and standardization
AI can scan thousands of documents such as sustainability reports and public filings and extract relevant ESG disclosures automatically. It then normalizes these data points across multiple frameworks, making them consistent and comparable across industries and geographies.
For example, a consultant advising a global client can rely on AI to map disclosures from subsidiaries reporting under different frameworks into a single, unified dataset. For investors, it means a consolidated view of portfolio exposure without weeks of manual reconciliation.
Enhance reliability and reduce greenwashing risk
Leading AI models can compare a company’s disclosures against industry benchmarks and flag anomalies that suggest overstatement or omission. If a firm claims a 40% emissions reduction in one year, the fact that this is well above sector averages would trigger the system to highlight the discrepancy for further review.
By reducing reliance on unverified claims, AI strengthens the credibility of both investor decisions and consultant advice.
Deliver actionable and forward-looking insights
Beyond cleaning data, AI enables instant analysis. Models can identify transition risks and highlight vulnerabilities in supply chains. For example, AI can flag that a company’s suppliers are concentrated in regions exposed to extreme weather, suggesting hidden risks to resilience.
For investors, this supports smarter due diligence and ongoing portfolio monitoring. For consultants, it enables scalable ESG audits, peer benchmarking, and actionable recommendations that go beyond static disclosures. Human oversight ensures these insights remain grounded and decision-useful.
How accurate ESG data strengthens decision-making
The outcomes of any decision-making process are entirely dependent on the quality of the inputs. In the case of ESG-related business decisions, without accurate, comprehensive ESG data, you’re not really making decisions — just guesses.
For investors, reliable insights mean stronger capital allocation, improved risk-adjusted returns, and greater confidence in portfolio resilience. It also supports engagement strategies, allowing investors to challenge companies with evidence-based questions.
For consultants, accuracy builds trust with clients and differentiates advisory services in a crowded market. Clients rely on data-backed insights to make regulatory, reputational, and strategic decisions.
Decision-useful ESG data has three key qualities:
- Completeness: no critical gaps or omissions
- Consistency: comparable across regions, industries, and time periods
- Actionability: insights that inform real business and investment decisions
When these conditions are met, ESG data becomes a driver of financial performance and stakeholder trust. Poor-quality data damages analyst models, erodes investor confidence, and undermines consultant credibility.
Getting started with AI-driven data management
For organizations ready to improve ESG data practices, the starting point should always be to assess existing systems and identify where manual processes create risk or inefficiency. From there, firms can pilot AI-powered tools to automate collection, validation, and conduct an ESG gap analysis.
A practical roadmap includes three steps:
- Audit current processes – Identify where ESG data is fragmented, outdated, or reliant on manual work.
- Pilot AI solutions – Start with one portfolio or client engagement to test automation and validation capabilities.
- Scale adoption – Integrate AI tools across compliance, advisory, and risk management systems for full coverage.
The goal is not to replace human expertise, but to augment it. Consultants and investors remain essential for interpreting results, tailoring recommendations, and engaging with stakeholders. AI provides the foundation (clean, reliable data at scale) that makes expert judgment more effective.
Manifest Climate’s AI-powered platform is designed specifically for this purpose. It centralizes ESG disclosures, aligns them across global standards, and delivers real-time insights that support compliance, risk management, and advisory excellence.
Strengthen ESG strategy resiliency with Manifest Climate
In an environment where ESG data challenges are intensifying, organizations need tools that go beyond compliance. Manifest Climate enables investors to de-risk portfolios and consultants to scale client services with confidence.
The advantages are clear:
- Faster due diligence and portfolio monitoring
- Audit-ready client reports backed by validated data
- Credible, comparable insights that strengthen decision-making
By turning ESG data from a fragmented burden into a reliable strategic asset, Manifest Climate helps financial and advisory firms gain a competitive edge.
Ready to see how it works? Book a demo today.
