AI, ESG and the Politics of Sustainable Investing

The sustainable finance landscape is becoming increasingly polarised. While Europe continues to attract billions into environmental, social and governance (ESG) funds, the US market has been gripped by an 11-quarter streak of outflows. Behind the numbers lies a story of politics, regulation, investor behaviour, and the growing role of artificial intelligence (AI) in making sense of it all.

The transatlantic split in ESG flows has become hard to ignore. Europe continues to see strong investor appetite, while the US has posted quarter after quarter of outflows. For Lorenzo Saa, Chief Sustainability Officer at Clarity AI, the drivers of this divide are layered and complex.

Lorenzo Saa, Chief Sustainability Officer at Clarity AI.

“In the US, the outflows stem from several overlapping forces. The political backlash grabs the headlines, pushing some investors to step back entirely. But beneath the surface, many are simply changing tactics: embedding ESG considerations quietly into mainstream funds without using the ‘ESG’ label: staying on course, just flying under the radar.”

A Transatlantic Divide

According to Saa, the US retreat is far from a wholesale rejection of sustainability. “Regulatory headwinds add another layer. With inconsistent disclosure standards, many asset managers don’t feel confident enough to lean into sustainability. And then there’s a natural correction of the hype, which, in my view, is a healthy recalibration rather than a retreat. At one point, it felt like everybody was doing sustainability; now the commitment requires more than just talk.”

This contrasts sharply with Europe, where regulation and investor appetite continue to pull in the same direction. “Europe, by contrast, continues to see steady inflows because regulation (even with the slowdown and Omnibus changes) and client demand act like twin engines pulling in the same direction. Higher levels of disclosure give European investors clearer visibility of where sustainability challenges lie, making it easier to keep capital flowing.”

Saa expects the divergence to persist in the near term, but not to spiral into a lasting gulf. “I don’t expect the gap to widen dramatically, but I do expect the divergence to persist in the near term. The federal stance is a brake, even as some states push the accelerator with stronger disclosure rules. Longer term, though, the US will realign with Europe. Sustainability risks don’t respect borders, and as they grow in scale and impact, smart investors won’t wait for perfect regulation; they’ll act on the data they have to stay ahead.”

AI as a Compass in a Fragmented Market

For investors straddling both markets, the challenge is not just political. Regulatory divergence creates operational friction, and that’s where AI is increasingly coming into play.

“AI is not a silver bullet. Political risks and growing policy divergence come from people and their voting choices, not from datasets,” Saa cautions. “But once those political choices translate into regulation, disclosure regimes, and supply chain rules, technology (with AI as a key component) becomes a critical companion. AI helps collect and process data at scale,

while flexible technology allows investors to compare across markets, spot inconsistencies, and manage the operational friction of fragmented rules.”

This goes beyond compliance. “In practice, AI can also support stress-testing by collecting and structuring the data needed to model different regulatory or political scenarios, something increasingly valuable as rules diverge.”

Furthermore, it also helps investors shine light on under-reported risks. “Climate and nature risks can be highly local, yet many US companies disclose less. Here, AI-powered tools that mine unstructured data from satellite images of deforestation to natural-language processing of local news reports can fill the gaps.

He exemplifies how, “Investors are already using AI to detect methane leaks in US oilfields or illegal logging in Brazil, issues that companies often under-report but satellites reveal in near real time. These insights give investors a stronger evidence base to challenge corporate disclosures.”

Another area of promise is taxonomy alignment. “Dozens of classification systems now exist, but the differences are often smaller than they appear. AI can map across these frameworks, helping investors cut through the noise and see where sustainable economic activity truly overlaps.”

Generative AI is starting to add another layer, particularly in regulatory reporting. “While AI helps firms collect, validate, and structure sustainability data at scale, generative AI now adds a complementary qualitative layer – transforming structured inputs into narratives that align with evolving regulatory expectations. This shift reduces the manual burden of tailoring disclosures to multiple regimes, while also enhancing the clarity and consistency of the underlying story that investors and regulators demand.”

From Scores to Context

One of the most striking shifts in the ESG data market is the move beyond static “scores” towards more dynamic, contextual insights. Saa sees AI as a catalyst here.

“Investors make decisions across the ‘knowledge pyramid’: from raw data, to information, to knowledge, to wisdom and insights. As they climb that pyramid, they get closer to the top, where the actual investment decisions are taken. AI can now support investors at every step.”

At the base, AI improves raw data quality and coverage. “It can then turn unstructured inputs (from satellite images showing forest loss to local news on labour disputes) into structured information that enhances or challenges company disclosures. Moving up the pyramid, AI transforms this into knowledge and wisdom: rather than collapsing sustainability into a single ‘rating’ or ‘score,’ it delivers dynamic company briefs, contextual analytics, and scenario-based insights, all traceable back to the original source. That traceability is a huge trust-builder.”

As disclosure standards vary across markets, AI can act as a translator. “Disclosure standards differ widely, but AI can act as a translator, linguistically and policy-wise, mapping fragmented data into comparable frameworks. Investors get a more consistent global view without waiting for regulators to align.”

On the question of bias, Saa strikes a balanced note. “Bias is often portrayed as a risk, and it is. It can stem from limited datasets or poorly designed models. But bias also exists in human judgement, and here AI can help counteract it. By training on balanced datasets, auditing outputs regularly, and pulling from a wide range of independent sources, AI reduces the blind spots of traditional sustainability analysis. The key is not to eliminate bias altogether, but to manage it consciously and transparently, something AI makes possible at scale.”

Navigating Tomorrow

Looking ahead, Saa expects political divides to narrow, and AI to accelerate convergence. “I expect today’s political divides around sustainability to narrow over time. Markets may look divergent now, but the forces of climate change and social risk are only becoming more material. AI will help accelerate convergence. I like to say AI will ‘MOP’ up sustainability challenges by Monitoring risks through satellite data and unstructured sources, Optimising systems like energy grids and supply chains, and Predicting both near-term shocks and long-term risks such as floods or sea-level rise. In doing so, it can wash away some of the mist that politics has thrown over this industry.”

For sceptical investors, AI may even depoliticise ESG. “AI-driven data makes analysis feel more rigorous and less politicised because it’s rooted in evidence, not labels. Instead of debating ideology, AI shows what’s actually happening on the ground: methane leaks from pipelines, deforestation visible from space, or supply-chain risks flagged in local news. It reframes sustainability as risk management and opportunity-spotting, not politics.”

Asked what advice he would give asset managers, Saa is clear: “Stay the course. Focus first on material sustainability issues, they will only grow in importance as the physical impacts of climate and social pressures mount. At the same time, know your clients. Some want to go further, investing not only to manage risks but to drive outcomes. Recognise those as different goals and be a responsible investor in the truest sense of the word: responder: to respond to client needs.”

For all the turbulence, the direction of travel seems clear. “While AI may be fiercely competitive at a geopolitical level, in practice it will act as a cross-border force for convergence around sustainability issues. Politics may set the tone today, but in the long run it’s physics and data that will decide the future of sustainable investing.”

“AI, ESG and the Politics of Sustainable Investing” was originally created and published by Private Banker International, a GlobalData owned brand.

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