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The future of AI is often framed in two extremes: Utopia or Dystopia. On the one hand, some consider that AI is being built for the betterment of human society. Meanwhile, others see it as inherently perpetuating an extractivist system that is dependent on unsustainable resource use.  Like a flint stone, a calculator, or the internet, AI is merely a tool. A tool’s power is given to it through the intentions and actions of its user.

In a world drowning in data, competitive advantage no longer comes from how much data you have — it comes from how well you govern it. The era of AI-driven data governance is here, and it’s reshaping how businesses make decisions, stay compliant, and protect sensitive information. Most organizations today are sitting on goldmines of data. But instead of being a strategic asset, data often becomes a liability.

In February 2025, the European Commission published a proposal, commonly referred to as the Omnibus proposal, to simplify EU sustainability reporting rules. If adopted, this would significantly narrow the scope of the European Sustainability Reporting Standards (ESRS), which were developed to support implementation of the Corporate Sustainability Reporting Directive (CSRD). Since the proposal’s announcement, many companies have slowed their CSRD compliance efforts.

With the recent developments in Generative AI (Gen AI) and Large Language Models (LLMs), there has been a flurry of discussions around the possible business use cases for these tools, as well as the risks. A promising area is in the ESG space, around data collection, analysis, and reporting. From our experience, while people are generally optimistic around AI's potential benefits, there is also a fundamental concern around what good data governance looks like.

"What do you mean we can't use AI in our business?" This is a common — and often incredulous — reaction I hear from prospective clients when I tell them that they can't use AI in their business. The surprise typically stems from a belief that they can simply point an AI at their data and it will magically figure out what to do and solve any problem. Unfortunately, AI isn't magic, it’s maths. If a human can't make sense of your data, it’s very unlikely that a machine can.

ESG consultants are under mounting pressure from all sides. Against a backdrop of intensifying climate change impacts while navigating regulatory confusion, their clients are asking for faster analysis and reporting, without compromising on quality and transparency.  However, instead of using their time to drive real sustainable action, analysts are stuck wrangling messy, scattered data, and manually mapping it onto complex ESG frameworks.

The recent and rapid development of Large Language Models (LLMs) has generated excitement over the various ways they can be applied ...

Can sycophants be good analysts? In the past two years, a lot has been said about the limitations of large language models (LLMs).

Back in 2005 at Goldmans, the technology we were developing was seen to be cutting edge. Goldman had invested billions of dollars in...

With this year’s exponential advancements in open-source AI, regulators and investors are now armed with tools for benchmarking and scrutiny

Presentation by the CEO of ImpactScope, Sean Murphy and Steve Cracknell, Chief Product Officer & Founder Director of INSIG AI at the...

In the rapidly evolving intersection of technology and corporate sustainability reporting, there is a growing role for specialised Large Lan

Mirroring the strong trend seen among large-cap companies, there has been significant growth (around 500%) in the number of ESG-related ...

Insig AI’s ESG disclosure data supports EY findings on lack of credible FTSE100 climate transition plans and reveals other sectoral and repo

The hype around Generative AI (GenAI) is real, and the advancements we will start to see with this technology being made widely accessible..