79% of companies have sustainability data, so why aren’t they using it in key decisions?

Your company is probably already measuring its sustainability impact. That’s not unusual: 79% of large companies do, and many have been collecting data for over a decade.

But measurement is no longer the problem. The real issue is what happens with that information.

In most cases, not much. Only between 20% and 25% of companies are using these data points in decisions related to operations, procurement, or product design. The rest remains within reporting.

This is not just a matter of sustainability maturity — it’s a structural misalignment. Companies are making decisions with a critical part of the information missing. And increasingly, that’s starting to have consequences.

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The rise of data hasn’t changed decision-making

Over the past decade, sustainability has evolved from a reputational concern into a business standard. The introduction of frameworks such as CSRD in Europe, the development of ESRS, and the consolidation of methodologies like the GHG Protocol have pushed companies to measure, structure, and report their impact with increasing levels of detail.

The result is an unprecedented volume of information: environmental indicators, social metrics, double materiality assessments, increasingly comprehensive emissions inventories… In many sectors, impact measurement is now as embedded as financial reporting.

However, this level of sophistication has not been matched in decision-making. While 79% of large companies already report their impact, only 20% to 25% are using that data in meaningful business decisions.

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What happens when you make decisions without that information

This disconnect is not just conceptual — it has very real day-to-day consequences:

• Seemingly cheaper purchases that become more expensive over time: suppliers with higher exposure to energy costs, carbon pricing, or regulatory risks that ultimately impact margins.
• Risks that surface too late: supply chain disruptions, cost increases, or regulatory changes that are not anticipated and force reactive, more expensive responses.
• Inefficiencies that erode profitability: resource- and energy-intensive processes that go unnoticed and accumulate into significant operational cost deviations.
• Missed commercial opportunities: failing to meet ESG requirements from clients, tenders, or investors, and being excluded from business opportunities.

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Why the data doesn’t reach the business

First, much of this data still lives outside operational workflows. It is generated by sustainability teams or external consultants, but not integrated into the systems where decisions are actually made.

This is compounded by the nature of reporting itself: aggregated, annual, and designed to explain what has already happened. A format that fits regulatory logic, but not day-to-day business operations.

Complexity also plays a key role. Especially in areas like Scope 3, where data is fragmented, dependent on third parties, and requires a level of processing capability that many organizations still lack.

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From reporting to a management tool

The challenge is not generating data, but integrating it into decision-making. This requires a shift in how data is used:

• From static reporting to operational input
• From annual analysis to continuous use
• From aggregated metrics to actionable insights
• From explaining the past to anticipating what’s ahead

Incorporating environmental variables into real decisions — such as supplier selection or process optimization — enables companies to detect risks earlier and uncover efficiency opportunities that would otherwise remain invisible.

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The role of technology: from data to action

The challenge is no longer measuring impact, but making sure that information is actually used where decisions are made. That is where Manglai introduces a clear shift by applying artificial intelligence to turn environmental data into operational decisions.

Its approach is directly tied to efficiency. The platform automates data collection, processing and analysis, significantly reducing the time spent on manual tasks. What previously took weeks of work can now be done much faster and with fewer errors, directly impacting costs.

From there, artificial intelligence helps identify inefficiencies that usually go unnoticed, such as processes with high resource consumption or suppliers with greater impact and associated risk. This makes it easier to make better decisions in procurement and supply chain management, optimizing costs not only in the short term but also avoiding future cost overruns.

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Want to see how it works?

Try Manglai’s free demo

Companies have already done a significant part of the journey. Now, the challenge is to move beyond reporting and start using that data to drive decisions.

Because in today’s context, the difference is not who measures — it’s who knows how to use that information to operate better.