In Data We Trust: Building a Sustainable Future on Reliable Information
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A conversation with Carlos Ribadeneira Espinoza, a leader in AI-driven data operations at Schneider Electric.
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In today’s data-driven world, the integrity of information is more critical than ever, especially when it comes to environmental reporting and sustainability metrics. Yet, organizations continue to grapple with the overwhelming volume and complexity of data, making it difficult to ensure accuracy, consistency, and trust. Traditional methods of data validation, often reliant on manual review, are no longer sufficient.
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Enter AI agents: intelligent, autonomous systems capable of reasoning, tool use, and granular data analysis. These agents are transforming how we approach data quality and large-scale sustainability challenges. By validating information line by line, such as verifying emissions data from procurement records or travel logs, and auditing their own decision-making processes, AI agents offer a level of precision and transparency that was previously unattainable with the manual methods most commonly used until now. Agents don’t just accelerate data processing; they elevate its completeness and reliability. Whether it’s selecting the correct emission factors, identifying anomalies in supplier data, or ensuring consistency across global reporting standards, AI agents are redefining what’s possible in data integrity and operational scale.
As climate goals grow more ambitious and regulatory expectations intensify, the ability to rely on your data becomes a prerequisite.
AI agents are not merely a technological upgrade, they represent a foundational shift in how organizations build confidence in the data that drives action.
Exploring the Future of AI in Sustainability Data
To understand how AI is reshaping sustainability data operations, we spoke with Carlos Ribadeneira Espinoza, a leader in AI-powered innovation at Schneider Electric. Carlos has been instrumental in developing intelligent agents that are redefining how sustainability data is validated, audited, and trusted.
“Data quality in sustainability and emissions reporting ultimately comes down to two things: trust and reliability, especially in the data that informs critical business and environmental decisions,” Carlos explains.
Throughout our conversation, Carlos highlighted how AI agents are doing more than accelerating data workflows, they're also enhancing the integrity of the data itself. These agents meticulously verify data at a granular level and conduct self-audits to ensure the integrity of their decisions. The ability to trust your data becomes a strategic differentiator.
From Manual Review to Machine Precision
We highlight a material distinction: AI agents are not just digital tools, but rather they're extensions of human capability, especially for repetitive, detail-heavy tasks that often bog down sustainability teams. A clear example of their sophisticated behavior is when they are deployed against data validation. Imagine needing to verify that a value like “1300” appears correctly on an invoice image. For a single invoice image, it’s not a problem. However, doing this review manually across thousands of invoices a day is not only tedious, it’s error prone. Fatigue sets in, and with it, the likelihood of misreading numbers, missing decimal points, or misinterpreting formatting differences across regions.
By contrast, AI agents don’t get tired. They can be instructed with both general and highly specific prompts, enabling them to perform these tasks with consistent accuracy. Their processes are repeatable, and their decisions are fully auditable. We can trace exactly how and why an agent made a decision, something that’s rarely documented in human workflows.
This shift doesn’t just improve accuracy, it accelerates it. In one of our data quality projects, a human might take two minutes to complete a comparison. An AI agent? Just 45 seconds. And with the rapid evolution of large language models, that speed is only improving. The result is a system that’s not only faster, but far more reliable, laying the groundwork for data you can trust, at scale.
Scaling Sustainability with Intelligence
The volume of energy and sustainability data is growing at an unprecedented pace. But rather than simplifying sustainability work, this data deluge is making it more complex. Sustainability teams are now navigating a dense and constantly evolving ecosystem of disclosure requirements, stakeholder expectations, and overlapping standards and frameworks. The result? A growing research and reporting burden that’s stretching teams thin and slowing progress.
As sustainability metrics become central to investment decisions and corporate strategy, the limitations of traditional methods, manual reviews, spreadsheets, and basic validation checks, are becoming increasingly apparent. These approaches simply can’t keep up with the scale, speed, and scrutiny required in today’s reporting environment. What’s needed is a smarter, more adaptive approach, one that blends automation with intelligence, and precision with transparency.
To meet this challenge, organizations must adopt systems that deliver two essential outcomes: speed and accuracy, while maintaining a clear, auditable trail of every decision made. This is especially critical as sustainability data becomes more tightly integrated with financial reporting, where the stakes include not only compliance, but also reputational and regulatory risk.
The sustainability sector is now turning to artificial intelligence to meet these demands. AI agents are being deployed to automate data validation, detect anomalies, and standardize inputs across diverse formats and sources. These systems can process vast datasets in seconds, flag inconsistencies in real time, and maintain detailed audit trails—making them indispensable for both compliance and decision-making.
Carlos illustrated the challenge:
“We receive thousands of invoices from different regions, each using different units and formats. A human has to learn all these variations. That’s a huge cognitive load.”
He continues:
“Many companies find manual data entry errors, typos, decimal misplacements—can significantly impact final calculations. AI agents help catch those issues early. They can even flag missing or incomplete data and reduce the time lag in verification.”
By embedding intelligence into the validation process, organizations can scale their sustainability operations without compromising on quality. AI agents don’t just lighten the load for human teams, they bring consistency, auditability, and speed to a process that’s foundational to credible disclosure reporting. For instance, agents are now being used to process Scope 3 emissions data, analyzing millions of purchase and travel records, selecting the right emission factors, and validating inputs line by line. This not only accelerates reporting but ensures every step is traceable and auditable. In a world where trust in data is everything, to remain top-of-mind with customers, intelligent automation should be a strategic necessity.
The Logic You Can See: AI Agents and the Rise of Transparent Data Workflows
As organizations face increasing scrutiny from regulators, investors, and the public, the ability to demonstrate how sustainability data is collected, validated, and reported has become essential. This is where AI agents offer a transformative advantage.
Unlike traditional manual processes, which often leave little trace of how decisions were made, AI agents operate with built-in traceability. Every step of their reasoning, every data point they reference, and every action they take can be logged and reviewed. This creates a clear, auditable trail that not only supports compliance but also builds trust with stakeholders.
A key innovation for ensuring successful data quality leveraging AI is the adoption of a multi-dimensional, hierarchical validation framework, a methodology that aligns with identifying layers where completeness and accuracy are optimized (Zhang et al., 2024). As Carlos explains, designing the agent on a hierarchical, multi-layer principle can make a big difference, especially in workflows that are clearly defined. In his application, the hierarchy of the layers is composed of a total check where the agent verifies high-level aggregate figures, such as total usage and cost. Subsequently, if needed, the agent performs a granular analysis of underlying components, including line items, determinants, and charges.
Finally, the agent performs a self-audit, asking, in effect, “Here’s what I’ve learned—am I confident in this?” This question is grounded in the instructions of the workflow. The agent evaluates its output using guidelines provided by business experts, such as to extract current charges instead of total charges when both are available, along with other similar instructions throughout the process. If the evaluation of components—based on context matches, values, currencies, or units of measure—seems inconsistent, ambiguous, or contradictory, the task is escalated to a human reviewer. This ensures that while AI handles the bulk of routine validation, human oversight remains essential for managing complexity and uncertainty. It’s a model of-collaborative intelligence, where machines and people work together to ensure both efficiency and accountability.
Beyond validation, AI agents are also transforming-intelligent document processing. With advanced natural language processing (NLP) and optical character recognition (OCR), agents can now extract structured data from unstructured formats like PDFs, scanned invoices, and emails; tasks that once required hours of manual effort. As these capabilities improve, agents are becoming increasingly adept at recognizing patterns, detecting anomalies, and even correcting errors in real time.
Carlos envisions a future where AI agents continuously monitor data streams, autonomously capture inconsistencies, and adapt their reasoning as models improve. While we’re not fully there yet, the trajectory is clear: AI agents are evolving from passive tools into proactive collaborators, capable of learning, auditing, and improving over time.
In this context, auditability isn’t just about oversight, it’s about evolution. By analyzing how agents make decisions, organizations can refine their data strategies, identify systemic issues, and continuously enhance the quality of their ESG reporting. Transparency becomes a catalyst for learning, not just a compliance checkbox.
As sustainability reporting continues to mature, the organizations that lead will be those that can not only move fast and scale smart, but also prove, with confidence and clarity, how they got there.
Risks and Limitations: Navigating the Boundaries of AI in Data Processing
AI agents are transforming the way organizations manage sustainability data by providing exceptional speed, accuracy, and auditability. However, despite their powerful capabilities, they still have limitations. As with any powerful tool, their use requires thoughtful oversight, ethical consideration, and a clear understanding of where human judgment must remain in the loop.
Bias in training data
One of the most fundamental challenges lies in the-inherent opacity of large language models (LLMs). These models are often trained on vast, proprietary datasets, making it difficult to fully understand the biases they may carry. As Carlos notes, “LLMs are like black boxes to some extent. We don’t always know what data they were trained on, and that can introduce bias, especially when working with non-Western languages or region-specific formats.”
Inconsistency in outputs
This becomes particularly evident in multilingual contexts. While LLMs are technically multilingual, their performance can vary significantly across languages. For instance, AI agents may misclassify information written in Mandarin or Hindi, even when the documents are clearly valid. These inconsistencies highlight the need for-language-aware validation strategies-and fallback mechanisms, such as translation layers or human review.
Dealing with stochasticity
Another key limitation is the inconsistency of AI outputs. LLMs are probabilistic by nature, they generate responses based on likelihood, not deterministic logic. This means that even when given the same input, an agent might produce slightly different outputs each time. While not necessarily incorrect, this variability can complicate testing, validation, and version control. “They’re not wrong,” Carlos explains, “just inconsistent. And that’s something we need to manage carefully.”
Environmental Impact
Another key challenge is keeping pace with the rapid evolution of AI technologies. With new models being released frequently, each claiming to be more accurate or efficient, organizations must continuously test and validate these updates to ensure they don’t introduce new errors or regressions. This creates a moving target for quality assurance teams and underscores the need for robust testing frameworks.
Perhaps most importantly, Carlos emphasizes the ethical and environmental considerations of AI deployment. “We talk a lot about frugal AI at Schneider Electric,” he says. “These models consume a lot of resources, so we need to be intentional about when and how we use them.” This means deploying AI where it adds the most value, while avoiding unnecessary computational overhead.
Human Oversight Needs
Finally, there’s the human factor. AI agents, no matter how advanced, are not a replacement for human expertise. Carlos likens them to “a brilliant junior employee” fast, capable, and eager to learn, but still in need of guidance. “You wouldn’t let a junior employee run your company,” he says. “They need context, oversight, and mentorship. The same goes for AI.”
In short, while AI agents offer immense potential, their deployment must be strategic, transparent, and human-centered. The goal isn’t to replace people, it’s to empower them with tools that extend their capabilities, while ensuring that trust, accountability, and ethical responsibility remain at the core of every decision.
Conclusion: From Insight to Action
As the sustainability landscape grows more complex, the need for intelligent, scalable, and trustworthy data systems has never been greater. AI agents are emerging as powerful allies; augmenting human capabilities, streamlining data validation, and enabling organizations to meet the rising demands of ESG reporting with speed, accuracy, and transparency.
Looking ahead, the future of data quality lies in-real-time, context-aware intelligence. Carlos envisions agents that go beyond simple reactions; they anticipate.
“Imagine an agent that knows how to address a late fee on an invoice because it’s been taught by our consultants’ past decisions and actions. That’s where we’re headed.”
But intelligence alone isn’t enough. Context is key.
“If we want an agent to behave like a human, we need to give it the same context a human would have. That’s how we make agents smarter and more reliable.”
This vision is already taking shape. At Schneider Electric, data operations agents are continuously being developed and refined, ensuring the company remains at the forefront of AI innovation in sustainability. By embedding collaborative intelligence into its core operations, Schneider Electric is improving data quality for faster, more confident decision-making across the sustainability value chain.
Want to learn more? Contact us today and let our global experts help guide you with strategy and technology that drives action and impact.