On 3 April 2025, the European Parliament voted to postpone the implementation deadlines of two major EU sustainability laws: the Corporate Sustainability Reporting Directive (CSRD) and the Corporate Sustainability Due Diligence Directive (CSDDD). The motion passed with an overwhelming majority of 531 votes in favor, 69 against, and 17 abstentions, supporting the European Commission’s “stop-the-clock” proposal. This vote, conducted under an urgent procedure, is part of a broader effort to streamline corporate sustainability requirements and reduce compliance burdens on companies. The Council of the EU had already endorsed the delay on 26 March 2025, citing the need to provide businesses with additional time to adapt to the directives. Final formal approval by the Council is expected shortly, after which the adjusted timelines will take effect.

Extended Deadline for Sustainability Reporting (CSRD)

The Corporate Sustainability Reporting Directive (CSRD) mandates companies to make extensive ESG disclosures. The approved delay affects the implementation timeline as follows:

• Large companies' reports delayed by 2 years: Companies defined as “large” under CSRD will now begin reporting on the financial year 2027, with the first sustainability reports published in 2028. Previously, these companies were expected to commence reporting for the financial year 2025, with reports published in 2026.
• Listed SMEs granted additional time: Listed small and medium-sized enterprises (SMEs) and other qualifying small companies will commence CSRD reporting one year later than initially scheduled, covering their financial year 2028 data in reports published in 2029. Under the original plan, these SMEs were to begin reporting for the financial year 2027, with an option to opt out until 2028.

Companies already within the scope of EU sustainability reporting (large public-interest entities under the previous Non-Financial Reporting Directive) are largely unaffected by this delay and have begun reporting for the financial year 2024 as planned. For the rest of the corporate sector, the CSRD’s effective start is deferred, providing additional time to build reporting systems and comply with the European Sustainability Reporting Standards (ESRS). The European Commission has tasked the European Financial Reporting Advisory Group (EFRAG) with simplifying and streamlining the reporting standards by late October 2025, enabling companies to adopt a more manageable set of disclosures when reporting begins.

One-Year Postponement for Due Diligence Rules (CSDDD)

The Parliament’s vote also extends the timeline for the Corporate Sustainability Due Diligence Directive (CSDDD), an EU law requiring companies to identify and mitigate human rights and environmental impacts in their operations and supply chains. The adopted delay includes:

• Transportation deadline extended: EU Member States now have until 26 July 2027 to transpose the CSDDD into national law, a one-year extension from the original July 2026 deadline. This extension allows governments to pass national legislation implementing the due diligence requirements.
• First corporate compliance phase delayed to 2028: The initial wave of companies subject to the CSDDD will have an additional year before the rules apply. Large EU firms with over 5,000 employees and €1.5 billion+ in turnover (and non-EU companies with equivalent EU turnover) must begin complying in July 2028 rather than 2027. Notably, this July 2028 phase will also cover companies with over 3,000 employees and €900 million turnover, effectively merging the directive’s first two implementation waves into one timeline.
• Subsequent phase in 2029: The next set of in-scope companies, including those with ≥1,000 employees and €450 million in turnover, are expected to come under the CSDDD by July 2029 as previously scheduled. The overall phase-in period is compressed into two stages (2028 and 2029) rather than spanning 2027–2029. This compressed rollout means the largest companies gain a one-year reprieve, while the smaller large companies will enter only slightly later than initially planned.
  

Next Steps

While this vote confirms a delay in implementation, negotiations regarding bigger changes to the laws (updating the reporting standards and the scope of companies affected) are still in their early stages. Those negotiations include exempting an estimated 80% of the companies initially covered by only applying these regulations only to firms with more than 1,000 employees. We delve deeper into these developments in our recent summary of the Omnibus initiative.

About SESAMm

SESAMm is a global leader in ESG controversy data, using advanced Generative AI. We automate monitoring and due diligence on public and private assets, providing coverage of more than 5 million companies. Our clients include companies like Carlyle, Warburg, Natixis, RBI, Fitch, Oddo, and more. SESAMm has raised $50M from renowned investors and operates across 4 continents.
Discover how we can help your team uncover ESG and reputational risks in seconds. Reques a free trial here.

Sources

• European Parliament Press Release:  Parlement Européen
• Reuters Article on EU Parliament Vote:  Reuters
• EFRAG Announcement on ESRS Simplification:  EFRAG

SESAMm’s AI Technology Reveals ESG Insights

Discover unparalleled insights into ESG controversies, risks, and opportunities across industries. Learn more about how SESAMm can help you analyze millions of private and public companies using AI-powered text analysis tools.

The secondaries market has tripled in size since 2019, now representing roughly $240 billion in annual volume. LP expectations around diligence - on exclusions, sanctions, mandate compliance, and reputational risk - have risen in lockstep. The window to screen a 300-company portfolio has not. For deal teams operating in an auction environment, the question is no longer how much to screen, but how to do it without becoming the reason a deal slips.

This brief distills key takeaways from a recent SESAMm webinar with Jessica Huang, Managing Director and ESG Lead for Private Equity and Secondaries at Ares Management, a firm managing over $40 billion in secondaries assets across four verticals.

It covers how screening requirements differ by transaction type, what investors are actually screening for, why private market data makes this hard, and what a workflow looks like that can realistically fit inside a 48-hour timeline.

Online, Japan Investor Forum, July 2022.

Sylvain Forté, SESAMm's co-founder and CEO, discusses ESG data and its challenges. Further, he describes how to generate insights and reports on millions of companies, including micro-companies, using artificial intelligence and natural language processing.

Below is an approximation of this video’s audio content. Watch the video for a better view of graphs, charts, graphics, images, and quotes the presenter might be referring to in context.

About SESAMm

To give you a bit of context, I’m CEO of SESAMm, a French company of around 100 people that has been in business for eight years and that specializes in artificial intelligence for finance, especially with a focus on ESG.

So we work with some of the largest insurance companies in Japan, such as Tokio Marine, Asset Management One, or Japan Post Insurance. And we have seen the rise of ESG investing in the past few years, especially in the past four years in Europe and in the U.S. And we see now this trend also in Asia and in Japan, more specifically.

Primary uses of ESG data

The primary uses of ESG that we see are first complying with regulation. That is the key priority for most asset managers, but also improving performance. Many quantitative teams are seeing ESG also as a way to have new factors integrated that could qualify to generate alpha in investment funds. ESG is also used a lot in order to better manage risk in portfolio and, finally, to better analyze sustainable investment opportunities.

ESG use cases

So a couple of the main use cases are detecting ESC controversies. So purely from the perspective of generating risk alerts, excluding assets that are not well rated in portfolios, or creating portfolios that contain best-in-class assets, meaning most sustainable assets.

And finally, I want to mention that this trend is really global. So it's across both public assets, equities, and bonds, and also across private equity. And we see private equity reacting very quickly to the ESG trend.

Traditional ESG data challenges

So now, let's discuss in more detail some of the key challenges of ESG data. Traditionally, ESG data is created by teams of analysts that are looking at individual companies that are gathering data from each of the companies, and that are then reading the press in order to complement that information. This approach is relevant, but it is hard to scale, and it presents some difficulty. Traditional ESG ratings agencies are, for example, MSCI or system analytics.

The problem with a lot of traditional ratings is that they don't cover small companies very well. And this is one of the key challenges currently in ESG is the lack of coverage. So it is very difficult to cover small caps, microcaps, and also private companies. In particular, in Asia, the coverage is very poor right now for ESG, and that means that many portfolio companies may not be covered by ESG rating. In Japan specifically, even large companies are sometimes not covered by traditional ESG providers. So that creates a lot of data inefficiency in the industry.

Another key challenge that we see in ESG right now is the frequency of ESG ratings. So oftentimes, ESG ratings are updated only one time per year or just a few times per year. And when ESG ratings are used for risk management, obviously, the market is moving much more quickly than one time or a few times per year.

In addition to that, we see that ESG ratings mostly takes into account information that is reported by management and does not take as much into account information that is from outside of the company. For example, in the case of government scandals, such as fraud scandals, it is actually better to have information that is not reported by the company but that also has an external point of view.

Lastly, the last key challenge I want to mention in ESG data specifically, and one challenge that I'm sure you are aware of in market data and fundamental data is that ESG data is oftentime, not point-in-time. So that means that you don't have a continuous dataset that has not been modified over time. ESG agencies tend to modify their ratings after the fact, and so that means that the rating that you will receive now for a data point in 2020 will not be the same that the rating that you would actually have received in 2020 point-in-time. That creates a lot of problems when you want to back-test data because you cannot reproduce actual historical results.

So these are all of the key challenges that we have identified in ESG data currently, and there are challenges in order to address the needs that we described. But there are actually some solutions that exist.

The solution to ESG data challenges

And one of the key solutions right now that is merging in ESG is the use of artificial intelligence, in particular, what is called natural language processing, meaning text analysis.

What we do at SESAMm and what some other providers do is detecting ESG risks and positive impact with regards to sustainability by analyzing automatically billions of articles and messages in real time. So as an example, we have 18 billion articles and messages from common news websites, from social media, from blogs and forums, and from company reports. And we automatically detect ESG themes and risk and perform sentiment analysis in order to understand whether a company may be exposed to an ESG controversy or whether a company may have positive impact with regards to sustainability.

Advantages of AI for ESG data challenges

And the advantage of AI in that context is that it solves a lot of the challenges that we discussed before. So it helps access higher frequency data, it helps cover small companies, private companies, it helps also find information that is independent, that is public, and that is not necessarily just reported by management, and it also is point-in-time information that can easily be backlisted.

How SESAMm tackles ESG data challenges

So I'll mention a couple of use cases to illustrate that in more detail. But basically, at SESAMm, we create an ESG datasets in order to track more than 90 different ESG risks and also the 17 sustainable development goals in order to precisely identify positive impact. And we do that on millions of companies, not just large public companies but also small caps and also private companies.

SESAMm ESG data use cases

Some of the use cases that I wanted to illustrate for that is using artificial intelligence in order to perform ESG monitoring using alerts. What that means is that we automatically generate ESG alerts on portfolios, for example, of equities or bonds on a daily basis, including portfolios of Japanese equities. And this data is then used by quantitative analysts and also fundamental managers to systematically exclude companies that are exposed to controversies in a portfolio. And this is a very efficient approach to systematically exclude companies that are not sustainable that are exposed to them.

Secondly, we have companies generate ESG signals by combining market data and ESG AI data to generate alpha. So basically, we create long-only and long-term portfolios, and we incorporate these ESG signals in order to improve the alpha of these portfolios.

The two last examples I wanted to mention, one is positive impact. So there is a specific framework called the UNSDGs for sustainable development goals, which is well suited to automatically detecting positive impact actions by a company, such as implementing, for example, a new net zero carbon policy. And we automatically track these announcements and these positive actions that companies perform in order, again, to share this information in the form of alerts to help fundamental managers track the sustainability actions of their portfolio companies and automatically report on them without having to do manual research.

The last use case I wanted to illustrate, and it's going to be my last point, is due diligence in private equity. So this is not only applicable to public assets but also to private assets. As an example, we have the Carlyle Group, a very large private equity company in particular with the Japanese team, and we have them generate various kinds of analytics at the stage when they evaluate the company. And in particular, we help them monitor and track potential ESG risk and sustainability factors which are very important to assess potential private assets opportunities. So this is the last use case that I want to mention. And as you can see, there are many opportunities in a growing field in ESG that started in Europe and came out to Asia. But there are also a lot of the challenges which artificial intelligence can help solve in some cases and which are illustrated with some examples.

Thank you very much.