Artificial intelligence has spent months at the centre of the corporate narrative. It promises efficiency, foresight, and competitive advantage. In sustainability too. Yet when it moves from headline to operations, the enthusiasm fades: one in three companies in Spain admits it does not know where to start, according to a study by the UN Global Compact.
This is not a question of willingness or access to technology. It is, above all, an issue of focus. Companies recognise the potential, but struggle to turn it into concrete decisions: what to apply, where to implement it, and what real impact it will have on the business.
The problem is not AI, it is not knowing what to use it for
The conversation around artificial intelligence in sustainability often starts too high up. There is talk of predictive models, automation, or complex algorithms, but it rarely comes down to what really matters: which specific business problem needs to be solved.
That's where the blockage appears.
The study "Artificial Intelligence and Companies: keys to advancing in sustainability" by the UN Global Compact in Spain puts numbers to this disconnect. 33% of organisations admit they do not know where to start when applying AI to their sustainability strategies. Only 12% have the knowledge, training, and resources required to do so, while the majority, 55%, remain at a basic understanding of its potential.
The problem, therefore, is not technological, but strategic. In day-to-day operations, this translates into very concrete barriers:
- Lack of clear use cases
- Difficulty integrating environmental data
- Uncertainty about return on investment
- An excess of solutions that lack practical grounding
More than a question of capability, what's missing is a clear understanding of what AI is for before deciding how to implement it. That's why, before choosing technology, it pays to be clear about how to choose the AI tool that solves your environmental challenges.
Where AI actually makes sense in sustainability
When you bring it down to concrete processes, the role of AI becomes much clearer. Today, its application in sustainability is mainly concentrated in three areas.
1. Measurement: turning scattered data into useful information
One of the biggest challenges is still data. AI can help automate data collection, detect inconsistencies, and fill gaps in incomplete datasets. It also makes it easier to work with standards such as the GHG Protocol or European reporting frameworks. The result is usually a more consistent and useful information base for analysis.
2. Reporting: moving from compliance to traceability
Sustainability reporting has gained significant weight in recent years. It is no longer just about compliance, but about being able to justify and explain the data.
Regulatory pressure has driven this shift. Regulations such as the CSRD in Europe, together with the ESRS standards, are raising the level of detail, consistency, and traceability required from companies. This is complemented by international frameworks like the GHG Protocol, which structure emissions measurement and reporting.
In this context, AI makes it possible to move towards more automated processes, with greater traceability and less reliance on manual tasks. It helps consolidate information, maintain consistency across indicators, and prepare data for audits or external verification.
3. Optimisation: making decisions with real impact
When environmental data is well structured, it stops being merely descriptive and starts guiding decisions. AI makes it possible to analyse large volumes of information to identify emission hotspots, both in internal processes and across the supply chain.
It also helps compare scenarios and anticipate the impact of different actions. For example, a company can assess how switching suppliers or adjusting logistics routes would affect its emissions and costs before making a decision.
This makes it possible to prioritise initiatives not only on environmental impact, but also on feasibility and return. In practice, AI does not replace decision-making, but it does reduce uncertainty and supports better judgment in complex environments.
Technology that supports, not complicates
In this context, the challenge is not understanding that AI can create value, but activating it in a way that is useful and aligned with the business. And this is where many companies fall short: they have data, they face regulatory pressure, they even have tools, but they fail to connect all of that into a system that actually works.
This is precisely where Manglai adds value.
Because the problem is not a lack of technology, but a lack of structure to turn sustainability into an operational process. Manglai helps bring AI down to what really matters: automating data collection, ensuring traceability, enabling compliant reporting, and, above all, transforming that information into actionable decisions.
Instead of starting from scratch or relying on fragmented solutions, companies can build on a foundation already designed to integrate ESG data, apply intelligence to it, and move forward with sound judgment. Without friction, without technical overload, and with clear business logic.
The result is not just better compliance, but better decision-making. Because when AI connects with real processes, it stops being an abstract promise and becomes a strategic tool. And that's when sustainability starts to generate tangible impact, both environmental and business. You can see how the Manglai AI platform applies this to environmental management.
