ESG today — why it can no longer be ignored
A decade ago, ESG — Environmental, Social, and Governance — was a niche concern, mostly relevant for large listed companies responding to investor questionnaires. Today, it has become a fundamental dimension of how organisations are evaluated, financed, and regulated.
Investors, regulators, customers, and employees are all asking the same question: is this organisation managing its impact on the world responsibly? And increasingly, they want data to answer it — not narrative, not pledges, but structured, auditable, comparable data.
The organisations that treat ESG as a reporting obligation — something to do once a year to satisfy regulators — are missing the point. ESG data, properly collected and analysed, is a management tool. It tells you where your operational risks are, where your supply chain is exposed, and where you have genuine competitive differentiation.
- ESG is no longer optional — regulatory requirements, investor pressure, and customer expectations have made it a baseline requirement for most organisations.
- The organisations winning on ESG are those treating it as a management discipline, not a compliance exercise.
- Data quality is the foundation. Greenwashing risk is highest where data is weakest.
The ESG data challenge — why it's hard
ESG reporting sounds straightforward until you try to do it. The data challenges are significant — and they're the reason most organisations struggle to produce ESG reports that are accurate, timely, and defensible.
ESG data lives everywhere: energy bills, HR systems, supplier questionnaires, logistics platforms, financial systems, facility management tools. No single system contains everything you need. Aggregating it manually is error-prone and takes months.
GRI, CSRD, TCFD, SASB, CDP, UN SDGs — the alphabet soup of ESG frameworks is genuinely overwhelming. Different stakeholders want different things. Maintaining multiple reporting frameworks from a single data source requires deliberate architecture.
For most organisations, Scope 3 emissions — those in the value chain, not under direct control — represent 70–90% of their carbon footprint. But they depend on suppliers providing accurate data, which most don't. Supply chain ESG data is the hardest problem in the field.
As ESG reporting becomes mandatory and legally binding, the data needs to be auditable. That means knowing exactly where every number came from, when it was collected, and who verified it. Manual Excel-based processes cannot provide this.
- ESG data is fundamentally a data integration and quality problem — the same disciplines that apply to any data platform apply here.
- Multi-framework reporting from a single source is the architecture goal — collect once, report many times.
- Audit trail requirements mean manual processes are not a long-term solution. Invest in structured data collection early.
Where AI adds real value in ESG
AI doesn't solve the ESG data problem — but it dramatically accelerates your ability to work with ESG data once the foundation is in place. Here are the areas where the value is clearest.
AI amplifies ESG capability — but it does not create it. An AI system applied to bad ESG data produces bad ESG insights faster. The data foundation must come first. AI is the accelerant, not the foundation.
- GenAI is most immediately useful for data extraction, report drafting, and consistency checking — low barrier to entry, high time savings.
- ML adds value for estimation, risk detection, and scenario modelling — higher investment, higher strategic value.
- Fix the data foundation before applying AI. AI on bad data produces confident-sounding wrong answers.
The regulatory landscape — what's coming
The regulatory environment for ESG is moving faster than most organisations' ability to respond. Here is a practical map of the key frameworks affecting European organisations.
| Framework | Who it applies to | Key requirement | Timeline |
|---|---|---|---|
| CSRD | Large EU companies (~50,000 companies phased) | Mandatory sustainability reporting under ESRS standards, third-party assurance required | 2024–2028 phased |
| EU Taxonomy | Large EU companies with NFRD obligations | Classify economic activities as environmentally sustainable, disclose alignment | In force |
| SFDR | Financial market participants in EU | Disclose sustainability risks and impacts in investment products | In force |
| TCFD | Varies by jurisdiction; increasingly mandatory | Disclose climate-related financial risks across four pillars | In force / expanding |
| GRI Standards | Voluntary but widely used globally | Comprehensive sustainability disclosure framework, sector-specific standards | Ongoing updates |
| CSDDD | Large EU and non-EU companies operating in EU | Due diligence on human rights and environmental impacts across value chain | 2027 onwards |
The Corporate Sustainability Reporting Directive is the most significant ESG regulatory development for European organisations in decades. It moves sustainability reporting from voluntary best practice to mandatory, audited disclosure — with the same rigour as financial reporting. If your organisation is in scope, the time to prepare your data infrastructure is now, not the year before your first filing.
- CSRD is the defining ESG regulation for European organisations — understand your scope and timeline now.
- The data infrastructure requirements of CSRD cannot be built in 6 months. Start the architecture work 2–3 years before your first mandatory filing.
- Third-party assurance requirements mean your ESG data needs to be auditable — not just reported.
From compliance to competitive advantage
The organisations that treat ESG as a compliance burden will spend significant resources producing reports that satisfy regulators but create no strategic value. The organisations that treat ESG as a management discipline will do something different.
They will use ESG data to make better operational decisions — identifying where energy waste is highest, where supply chain exposure is concentrated, where workforce practices create retention risk. The same data that goes into a sustainability report can also drive operational improvement, if the data infrastructure is built correctly.
The organisations extracting strategic value from ESG data share a common architecture principle: collect ESG data at the operational level, not just for reporting. Energy data collected monthly for a carbon report is also operational data that can trigger efficiency interventions in real time. Supplier ESG scores collected for CSRD compliance are also procurement risk signals. The data is the same — the difference is whether the infrastructure is built to use it operationally or just to report it annually.
This is the shift I championed at datalitiks under the "Data for Good" concept: ESG data is not a reporting output — it's a strategic input. The organisations that internalise this will have a genuine competitive advantage as ESG requirements intensify and data quality becomes a differentiator.
- Build ESG data infrastructure to serve operational decisions, not just annual reports — the marginal cost of doing both is low.
- ESG data quality will become a competitive differentiator as mandatory assurance requirements raise the bar for everyone.
- The "Data for Good" principle: sustainability goals and data excellence reinforce each other — organisations with better data make better sustainability decisions.
datalitiks — lessons from building an ESG AI platform
This module is the most personal one in the Learning Hub, because it comes directly from founding and running datalitiks — an ESG data and analytics platform built from scratch in Malta between 2022 and 2024.
datalitiks was built on a simple premise: organisations needed a way to transform fragmented ESG data into structured, actionable insights — and they needed it to work across multiple reporting frameworks simultaneously. The platform collected ESG data from multiple operational sources, structured it against GRI, UN SDGs, and sector-specific standards, and produced reporting-ready outputs.
We integrated ChatGPT from week one — not as an experiment, but as a core part of how we built and operated the platform. The results were transformative for a lean team: ~30% faster platform development, ~40% lower content production costs, and the ability to deliver the equivalent of a much larger team's output. The key was integrating AI into every workflow systematically — content production, platform development, client communication, framework analysis — rather than using it occasionally for one-off tasks.
Three things I would do differently. First: data standardisation is harder than it looks. Getting clients to provide ESG data in a consistent, structured format required more change management than technical work. Second: regulatory requirements move faster than product roadmaps. CSRD scope and requirements shifted significantly during our development period — build flexibility into your data architecture. Third: the market education burden is significant. In 2022, most organisations did not yet understand why they needed structured ESG data. That has changed dramatically — but early movers in this space need patience.