Rémy joined Integrum ESG in 2022 to oversee research in Machine Learning, Natural Language Processing and AI capabilities. Prior to joining Rémy was a Research Fellow at Harvard. He holds an MSc in Mathematical Statistics from ENSAE Paris (Institut Polytechnique) and an MSc in Economics and Management from ESSEC Business School.
How Artificial Intelligence Is Reshaping ESG Data, Performance and Governance Globally.
Over the past year, the relationship between artificial intelligence and ESG has shifted decisively from tentative experimentation to operational necessity across major financial markets.
What was once approached with scepticism driven by concerns around complexity, credibility and risk is now widely recognised as essential for capturing, analysing and reporting ESG data at the scale, speed and granularity required by investors and regulators in Europe, the UK, North America and Asia-Pacific.
At the same time, organisations operating across jurisdictions have learned that moving from compelling proofs of concept to production-grade AI systems is non-trivial. Delivering ESG intelligence that is accurate, explainable and auditable requires more than generic AI tools. It demands robust data foundations, strong governance and deep domain expertise that can withstand regulatory scrutiny across regions.
This growing maturity sets the context for how AI has reshaped and will continue to reshape ESG performance, assessment and investment decision-making globally.
As an AI-driven ESG intelligence platform, Integrum ESG observes that artificial intelligence now cuts across every dimension of Environmental, Social and Governance performance in both developed and emerging markets.
By 2025, AI could no longer be assessed in isolation. It emerged simultaneously as a sustainability risk, an operational enabler and a governance challenge for companies subject to frameworks such as CSRD, UK Sustainability Disclosure Standards and evolving US and Asia-Pacific reporting regimes.
Looking ahead to 2026, AI’s influence on ESG outcomes is expected to deepen further across all three pillars as regulatory expectations converge and cross-border investment scrutiny intensifies.
In 2025, AI faced increased scrutiny for its environmental footprint across regions with differing energy costs and regulatory pressures. Energy and water consumption linked to data centres, model training and inference became more visible to regulators, investors and the public in Europe, North America and parts of Asia.
At the same time, AI delivered measurable environmental benefits. Across buildings, manufacturing and logistics, AI-driven optimisation improved energy efficiency, reduced waste and lowered emissions. These use cases proved particularly relevant for organisations operating complex multinational supply chains.
The year highlighted AI’s dual environmental role as both a source of environmental pressure and a powerful tool for mitigation.
Looking ahead to 2026, AI is expected to become more deeply embedded across global supply chains, improving environmental visibility, scenario modelling and climate-risk management in line with jurisdiction-specific disclosure requirements.
Beyond carbon, AI-driven analytics for nature, land use and biodiversity are likely to mature, supporting alignment with emerging global frameworks and regionally mandated nature-related disclosures.
In 2025, organisations learned that responsible AI deployment in production systems requires significantly more effort than the proof-of-concepts enabled by widely adopted generative AI tools.
While experimentation with systems such as ChatGPT delivered rapid and compelling results, translating those capabilities into secure, reliable and scalable environments proved far more complex. As AI scaled across workforces and customer-facing applications, issues around data security, privacy, bias and model behaviour became increasingly visible across jurisdictions with differing labour laws and data protection regimes.
In 2026, workforce disruption and displacement are expected to intensify scrutiny of reskilling, job quality and workforce equity, particularly in regions with strong social regulation and union representation.
How organisations manage transitions, protect workers and measure social impact will become a core ESG consideration globally. At the same time, employee data privacy is likely to emerge as a central social-impact debate as AI systems increasingly rely on behavioural, performance and productivity data subject to region-specific privacy rules.
Governance experienced the most tangible shift in 2025 across major capital markets.
Boards moved beyond high-level AI ethics statements toward practical oversight as investors and regulators demanded explainable, auditable and well-controlled AI systems, particularly where AI supports ESG reporting, risk assessment or strategic decision-making.
In 2026, mandatory AI audits are expected to expand across regions, supported by growing third-party AI assurance capabilities and increased alignment with international standards such as ISO 42001.
Organisations with integrated AI governance spanning risk management, controls, documentation and ESG alignment will increasingly differentiate themselves as credible operators across multiple regulatory environments.
For investors, 2025 made one point clear. ESG and AI claims now require substance regardless of geography.
The focus has shifted to whether AI systems are economically efficient, environmentally defensible and governed to a standard that withstands regulatory and assurance scrutiny across jurisdictions.
In 2026, attention is expected to move beyond AI systems that simply retrieve and summarise ESG data. Investors will increasingly focus on intelligence engines and agent-based systems that support deeper analysis, forecasting and decision-making across global operations.
As AI is applied to energy use, materials, logistics and operations, ESG insights are becoming directly linked to margins, productivity and operational risk rather than remaining confined to disclosure obligations in any single market.
This creates a clear distinction between companies experimenting with AI and those deploying production-grade ESG intelligence systems at scale.
The latter are more likely to rely on specialist third-party providers that combine ESG domain expertise, robust data infrastructure and governed AI models rather than bespoke in-house solutions built around general-purpose tools.
Organisations that can demonstrate AI-driven ESG insights that are timely, auditable and financially relevant across regions are better positioned to manage risk, improve efficiency and sustain long-term value creation.
ESG performance will increasingly be shaped by the quality of AI embedded across operations, supply chains and decision-making processes globally rather than by the volume of AI experimentation.
Investors assessing AI and ESG alignment should:
Assess whether AI systems used in ESG analysis are explainable, auditable and appropriately governed across regulatory regimes rather than relying on high-level claims of capability
Evaluate AI as both an ESG risk and an operational enabler by considering environmental footprint, workforce impact and governance controls in different jurisdictions
Distinguish between AI experimentation and production-grade deployment by favouring companies that link AI-driven ESG insights to financial performance and risk management across global operations
Browse frequently asked questions about the platform.
Can’t find the answer you’re looking for? Please get in touch with our team.
