The built environment, including our homes, offices, factories, and infrastructure, is responsible for approximately 40% of global carbon emissions. Reducing that impact is one of the greatest sustainability challenges of our time, but it is also one of the most complex.
At the center of this challenge lies data.
Product data is inherently messy. A single carbon footprint calculation can draw on material declarations, supplier EPDs, transport distances, energy consumption, and manufacturing process data, all delivered in different formats. Units rarely match, values may be outdated, and supplier assumptions often vary widely. Yet this information forms the foundation of sustainability reporting, compliance, procurement, and decision-making across the built environment.
Why Change Is Urgent
Organizations face increasing pressure to improve sustainability performance.
Investors are evaluating businesses based on their climate impact and long-term resilience. Governments are introducing stricter regulations and reporting requirements. Customers, citizens, and industry stakeholders increasingly expect transparency and measurable progress.
Regulations such as the Corporate Sustainability Reporting Directive (CSRD) are accelerating this shift by requiring organizations to report environmental impacts with greater accuracy, consistency, and traceability, including indirect emissions such as Scope 3.
Sustainability is no longer simply a corporate responsibility initiative. It has become a strategic, operational, and financial imperative.
The Role of Data in Sustainability
High-quality data is the foundation of effective climate action.
Across the built environment, information is generated from a growing number of sources, including IoT sensors, building management systems, supplier declarations, lifecycle assessments (LCAs), Environmental Product Declarations (EPDs), procurement systems, and operational data.
The challenge is that this information is often fragmented, inconsistent, or unstructured. Different systems describe similar data in different ways. Information must frequently be manually verified, transformed, and consolidated before it can be used.
Poor data quality leads to poor decisions. It can undermine sustainability strategies, create compliance risks, and prevent organizations from identifying meaningful opportunities to reduce emissions.
How AI Can Help
Artificial intelligence has the potential to transform how organizations manage sustainability data.
AI can optimize building operations by improving energy consumption, heating, cooling, and lighting performance. It can increase supply chain transparency by analyzing large datasets across suppliers, materials, and logistics networks. It can support sustainable design decisions through predictive modeling and scenario analysis. And it can enable real-time decision-making by continuously identifying risks, opportunities, and inefficiencies.
Perhaps most importantly, AI can process and connect information at a scale that would be impossible through manual effort alone.
Tackling Scope 3 Emissions
Scope 3 emissions, those generated across an organization's broader value chain, are among the most difficult emissions to measure and manage.
They often depend on data originating from suppliers, manufacturers, transportation providers, and material producers. Gathering, validating, and maintaining this information is a significant challenge.
AI can help organizations analyze these complex datasets, identify data gaps, estimate missing values, and improve the accuracy and consistency of emissions calculations. This makes it possible to understand environmental impacts more comprehensively and make better-informed decisions.
Why Intelligent Product Data Matters
While AI has enormous potential, its effectiveness depends entirely on the quality of the data it receives.
Today, much of the industry's product information remains locked in documents, fragmented across formats, and maintained through manual processes. At the same time, AI requires machine-readable, structured data. Regulations require traceability and consistency. Digital workflows depend on interoperability. And every stakeholder still needs information delivered in different formats and contexts.
This creates a growing gap between how product data exists today and how it needs to function in the future.
Closing that gap requires a new approach: Intelligent Product Data.
Intelligent Product Data is structured, connected, traceable, and designed to move seamlessly between systems. It enables information to be understood by both people and machines, supports regulatory compliance, improves sustainability reporting, and creates the foundation for AI-driven workflows.
The future of sustainable transformation will not be built on AI alone. It will be built on AI powered by reliable, interoperable, and intelligent product data.
Because better decisions begin with better data and increasingly, those decisions will be made by intelligent systems.